{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "085feda9",
   "metadata": {},
   "source": [
    "## Resnet on CIFAR10\n",
    "\n",
    "在[resnet.ipynb](./resnet.ipynb)中我们测试了Resnet在Imagenette上的分类任务, Imagenette是只包含10类的ImageNet的子集,这里我们在CIFAR10上测试对比不同的Resnet网络效果.\n",
    "\n",
    "CIFAR-10数据集共有60000张彩色图像，这些图像是32*32，分为10个类，每类6000张图，5000张用于训练，1000张用于测试。10 个不同的类别分别代表飞机、汽车、鸟、猫、鹿、狗、青蛙、马、船和卡车。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e15b3c33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use device:  cuda\n"
     ]
    }
   ],
   "source": [
    "# 自动重新加载外部module，使得修改代码之后无需重新import\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from hdd.device.utils import get_device\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# 设置训练数据的路径\n",
    "DATA_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置TensorBoard的路径\n",
    "TENSORBOARD_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置预训练模型参数路径\n",
    "TORCH_HUB_PATH = \"~/workspace/hands-dirty-on-dl/pretrained_models\"\n",
    "torch.hub.set_dir(TORCH_HUB_PATH)\n",
    "# 挑选最合适的训练设备\n",
    "DEVICE = get_device([\"cuda\", \"cpu\"])\n",
    "print(\"Use device: \", DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c08db432",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "Basic Info of train dataaset: \n",
      " Dataset CIFAR10\n",
      "    Number of datapoints: 50000\n",
      "    Root location: /home/tf/workspace/hands-dirty-on-dl/dataset\n",
      "    Split: Train\n",
      "    StandardTransform\n",
      "Transform: Compose(\n",
      "               Pad(padding=4, fill=0, padding_mode=constant)\n",
      "               RandomRotation(degrees=[-3.0, 3.0], interpolation=nearest, expand=False, fill=0)\n",
      "               RandomCrop(size=(32, 32), padding=None)\n",
      "               RandomHorizontalFlip(p=0.5)\n",
      "               ToTensor()\n",
      "               Normalize(mean=[0.49139968, 0.48215827, 0.44653124], std=[0.24703233, 0.24348505, 0.26158768])\n",
      "           )\n",
      "Basic Info of test dataset: \n",
      " Dataset CIFAR10\n",
      "    Number of datapoints: 10000\n",
      "    Root location: /home/tf/workspace/hands-dirty-on-dl/dataset\n",
      "    Split: Test\n",
      "    StandardTransform\n",
      "Transform: Compose(\n",
      "               ToTensor()\n",
      "               Normalize(mean=[0.49139968, 0.48215827, 0.44653124], std=[0.24703233, 0.24348505, 0.26158768])\n",
      "           )\n"
     ]
    }
   ],
   "source": [
    "from hdd.data_util.transforms import RandomResize\n",
    "\n",
    "# 我们提前计算好了训练数据集上的均值和方差\n",
    "TRAIN_MEAN = [0.49139968, 0.48215827, 0.44653124]\n",
    "TRAIN_STD = [0.24703233, 0.24348505, 0.26158768]\n",
    "\n",
    "train_dataset_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.Pad(4),\n",
    "        transforms.RandomRotation(3),\n",
    "        transforms.RandomCrop(32),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "    ]\n",
    ")\n",
    "# 加载数据集\n",
    "train_dataset = datasets.CIFAR10(\n",
    "    root=DATA_ROOT,\n",
    "    train=True,\n",
    "    transform=train_dataset_transforms,\n",
    "    download=True,\n",
    ")\n",
    "val_dataset = datasets.CIFAR10(\n",
    "    root=DATA_ROOT,\n",
    "    train=False,\n",
    "    transform=transforms.Compose(\n",
    "        [transforms.ToTensor(), transforms.Normalize(TRAIN_MEAN, TRAIN_STD)]\n",
    "    ),\n",
    "    download=True,\n",
    ")\n",
    "print(\"Basic Info of train dataaset: \\n\", train_dataset)\n",
    "print(\"Basic Info of test dataset: \\n\", val_dataset)\n",
    "BATCH_SIZE = 128\n",
    "train_dataloader = torch.utils.data.DataLoader(\n",
    "    train_dataset,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=True,\n",
    "    num_workers=4,\n",
    ")\n",
    "val_dataloader = torch.utils.data.DataLoader(\n",
    "    val_dataset,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    shuffle=False,\n",
    "    num_workers=4,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a9ad906",
   "metadata": {},
   "source": [
    "## 测试比较不同的Resnet架构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3ccc11ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/160 Train Loss: 1.5754 Accuracy: 0.4147 Time: 6.51437  | Val Loss: 1.3648 Accuracy: 0.5173\n",
      "Epoch: 2/160 Train Loss: 1.0955 Accuracy: 0.6071 Time: 5.89876  | Val Loss: 1.5117 Accuracy: 0.5157\n",
      "Epoch: 3/160 Train Loss: 0.9102 Accuracy: 0.6813 Time: 6.10664  | Val Loss: 1.4193 Accuracy: 0.5716\n",
      "Epoch: 4/160 Train Loss: 0.7826 Accuracy: 0.7295 Time: 5.82136  | Val Loss: 0.7988 Accuracy: 0.7389\n",
      "Epoch: 5/160 Train Loss: 0.7242 Accuracy: 0.7537 Time: 5.91474  | Val Loss: 0.9694 Accuracy: 0.7021\n",
      "Epoch: 6/160 Train Loss: 0.6803 Accuracy: 0.7686 Time: 5.99791  | Val Loss: 0.8352 Accuracy: 0.7249\n",
      "Epoch: 7/160 Train Loss: 0.6523 Accuracy: 0.7791 Time: 5.94631  | Val Loss: 1.2362 Accuracy: 0.6403\n",
      "Epoch: 8/160 Train Loss: 0.6344 Accuracy: 0.7860 Time: 5.97014  | Val Loss: 0.7764 Accuracy: 0.7465\n",
      "Epoch: 9/160 Train Loss: 0.6237 Accuracy: 0.7905 Time: 6.16274  | Val Loss: 0.7315 Accuracy: 0.7629\n",
      "Epoch: 10/160 Train Loss: 0.6098 Accuracy: 0.7919 Time: 5.96505  | Val Loss: 1.5264 Accuracy: 0.6276\n",
      "Epoch: 11/160 Train Loss: 0.6072 Accuracy: 0.7939 Time: 6.00884  | Val Loss: 1.0577 Accuracy: 0.6765\n",
      "Epoch: 12/160 Train Loss: 0.5982 Accuracy: 0.7975 Time: 5.99927  | Val Loss: 0.7633 Accuracy: 0.7504\n",
      "Epoch: 13/160 Train Loss: 0.5961 Accuracy: 0.8003 Time: 6.13146  | Val Loss: 0.7811 Accuracy: 0.7364\n",
      "Epoch: 14/160 Train Loss: 0.5971 Accuracy: 0.7991 Time: 5.77616  | Val Loss: 0.7730 Accuracy: 0.7547\n",
      "Epoch: 15/160 Train Loss: 0.5944 Accuracy: 0.7999 Time: 5.68268  | Val Loss: 0.8305 Accuracy: 0.7334\n",
      "Epoch: 16/160 Train Loss: 0.5899 Accuracy: 0.8026 Time: 5.68093  | Val Loss: 0.8930 Accuracy: 0.7368\n",
      "Epoch: 17/160 Train Loss: 0.5897 Accuracy: 0.8014 Time: 5.80299  | Val Loss: 0.9906 Accuracy: 0.7120\n",
      "Epoch: 18/160 Train Loss: 0.5814 Accuracy: 0.8037 Time: 5.73998  | Val Loss: 0.7775 Accuracy: 0.7421\n",
      "Epoch: 19/160 Train Loss: 0.5801 Accuracy: 0.8038 Time: 5.75111  | Val Loss: 0.7169 Accuracy: 0.7595\n",
      "Epoch: 20/160 Train Loss: 0.5861 Accuracy: 0.8002 Time: 6.05597  | Val Loss: 0.8144 Accuracy: 0.7530\n",
      "Epoch: 21/160 Train Loss: 0.5815 Accuracy: 0.8055 Time: 5.83376  | Val Loss: 0.6548 Accuracy: 0.7835\n",
      "Epoch: 22/160 Train Loss: 0.5850 Accuracy: 0.8039 Time: 5.88360  | Val Loss: 0.8818 Accuracy: 0.7153\n",
      "Epoch: 23/160 Train Loss: 0.5827 Accuracy: 0.8052 Time: 5.91087  | Val Loss: 0.7857 Accuracy: 0.7407\n",
      "Epoch: 24/160 Train Loss: 0.5849 Accuracy: 0.8035 Time: 5.77111  | Val Loss: 0.7951 Accuracy: 0.7430\n",
      "Epoch: 25/160 Train Loss: 0.5738 Accuracy: 0.8055 Time: 5.92673  | Val Loss: 0.7577 Accuracy: 0.7447\n",
      "Epoch: 26/160 Train Loss: 0.5858 Accuracy: 0.8029 Time: 5.85064  | Val Loss: 1.2045 Accuracy: 0.6480\n",
      "Epoch: 27/160 Train Loss: 0.5828 Accuracy: 0.8040 Time: 5.76039  | Val Loss: 0.9293 Accuracy: 0.7090\n",
      "Epoch: 28/160 Train Loss: 0.5688 Accuracy: 0.8092 Time: 5.96292  | Val Loss: 0.9254 Accuracy: 0.6976\n",
      "Epoch: 29/160 Train Loss: 0.5749 Accuracy: 0.8051 Time: 5.84906  | Val Loss: 0.6961 Accuracy: 0.7665\n",
      "Epoch: 30/160 Train Loss: 0.5765 Accuracy: 0.8059 Time: 5.76406  | Val Loss: 0.9281 Accuracy: 0.7035\n",
      "Epoch: 31/160 Train Loss: 0.5714 Accuracy: 0.8087 Time: 5.74950  | Val Loss: 0.8850 Accuracy: 0.7340\n",
      "Epoch: 32/160 Train Loss: 0.5733 Accuracy: 0.8068 Time: 5.87449  | Val Loss: 0.6309 Accuracy: 0.7936\n",
      "Epoch: 33/160 Train Loss: 0.5709 Accuracy: 0.8083 Time: 5.70755  | Val Loss: 0.9398 Accuracy: 0.6988\n",
      "Epoch: 34/160 Train Loss: 0.5706 Accuracy: 0.8082 Time: 5.70791  | Val Loss: 0.8399 Accuracy: 0.7243\n",
      "Epoch: 35/160 Train Loss: 0.5762 Accuracy: 0.8044 Time: 5.87666  | Val Loss: 0.7880 Accuracy: 0.7544\n",
      "Epoch: 36/160 Train Loss: 0.5729 Accuracy: 0.8061 Time: 5.94787  | Val Loss: 0.8587 Accuracy: 0.7409\n",
      "Epoch: 37/160 Train Loss: 0.5645 Accuracy: 0.8097 Time: 5.96095  | Val Loss: 0.9512 Accuracy: 0.7056\n",
      "Epoch: 38/160 Train Loss: 0.5654 Accuracy: 0.8107 Time: 5.84717  | Val Loss: 0.6524 Accuracy: 0.7850\n",
      "Epoch: 39/160 Train Loss: 0.5696 Accuracy: 0.8082 Time: 5.79803  | Val Loss: 1.2833 Accuracy: 0.6689\n",
      "Epoch: 40/160 Train Loss: 0.5745 Accuracy: 0.8069 Time: 6.01574  | Val Loss: 0.7202 Accuracy: 0.7689\n",
      "Epoch: 41/160 Train Loss: 0.3579 Accuracy: 0.8790 Time: 5.81366  | Val Loss: 0.3169 Accuracy: 0.8907\n",
      "Epoch: 42/160 Train Loss: 0.2924 Accuracy: 0.9008 Time: 6.02080  | Val Loss: 0.3088 Accuracy: 0.8970\n",
      "Epoch: 43/160 Train Loss: 0.2703 Accuracy: 0.9076 Time: 5.99178  | Val Loss: 0.3006 Accuracy: 0.8993\n",
      "Epoch: 44/160 Train Loss: 0.2482 Accuracy: 0.9163 Time: 6.01479  | Val Loss: 0.2945 Accuracy: 0.9001\n",
      "Epoch: 45/160 Train Loss: 0.2349 Accuracy: 0.9195 Time: 5.94499  | Val Loss: 0.2925 Accuracy: 0.9006\n",
      "Epoch: 46/160 Train Loss: 0.2230 Accuracy: 0.9228 Time: 6.00173  | Val Loss: 0.2952 Accuracy: 0.9042\n",
      "Epoch: 47/160 Train Loss: 0.2087 Accuracy: 0.9287 Time: 5.83137  | Val Loss: 0.2867 Accuracy: 0.9053\n",
      "Epoch: 48/160 Train Loss: 0.1998 Accuracy: 0.9316 Time: 5.83882  | Val Loss: 0.2883 Accuracy: 0.9057\n",
      "Epoch: 49/160 Train Loss: 0.1905 Accuracy: 0.9341 Time: 5.82116  | Val Loss: 0.2752 Accuracy: 0.9087\n",
      "Epoch: 50/160 Train Loss: 0.1838 Accuracy: 0.9372 Time: 5.62691  | Val Loss: 0.2847 Accuracy: 0.9083\n",
      "Epoch: 51/160 Train Loss: 0.1744 Accuracy: 0.9401 Time: 5.87189  | Val Loss: 0.2846 Accuracy: 0.9073\n",
      "Epoch: 52/160 Train Loss: 0.1720 Accuracy: 0.9410 Time: 5.86875  | Val Loss: 0.2773 Accuracy: 0.9109\n",
      "Epoch: 53/160 Train Loss: 0.1629 Accuracy: 0.9445 Time: 5.73740  | Val Loss: 0.2877 Accuracy: 0.9084\n",
      "Epoch: 54/160 Train Loss: 0.1614 Accuracy: 0.9428 Time: 5.81649  | Val Loss: 0.2848 Accuracy: 0.9098\n",
      "Epoch: 55/160 Train Loss: 0.1597 Accuracy: 0.9447 Time: 5.86896  | Val Loss: 0.3034 Accuracy: 0.9076\n",
      "Epoch: 56/160 Train Loss: 0.1473 Accuracy: 0.9487 Time: 6.02577  | Val Loss: 0.3162 Accuracy: 0.9063\n",
      "Epoch: 57/160 Train Loss: 0.1507 Accuracy: 0.9481 Time: 5.84680  | Val Loss: 0.3075 Accuracy: 0.9083\n",
      "Epoch: 58/160 Train Loss: 0.1479 Accuracy: 0.9496 Time: 5.72079  | Val Loss: 0.2838 Accuracy: 0.9126\n",
      "Epoch: 59/160 Train Loss: 0.1458 Accuracy: 0.9495 Time: 5.88021  | Val Loss: 0.3173 Accuracy: 0.9043\n",
      "Epoch: 60/160 Train Loss: 0.1456 Accuracy: 0.9487 Time: 5.84739  | Val Loss: 0.3101 Accuracy: 0.9093\n",
      "Epoch: 61/160 Train Loss: 0.1425 Accuracy: 0.9500 Time: 5.70976  | Val Loss: 0.3020 Accuracy: 0.9089\n",
      "Epoch: 62/160 Train Loss: 0.1395 Accuracy: 0.9521 Time: 5.92931  | Val Loss: 0.3255 Accuracy: 0.9059\n",
      "Epoch: 63/160 Train Loss: 0.1407 Accuracy: 0.9518 Time: 5.72741  | Val Loss: 0.3714 Accuracy: 0.8933\n",
      "Epoch: 64/160 Train Loss: 0.1410 Accuracy: 0.9508 Time: 5.97208  | Val Loss: 0.3476 Accuracy: 0.9017\n",
      "Epoch: 65/160 Train Loss: 0.1407 Accuracy: 0.9527 Time: 5.75918  | Val Loss: 0.3665 Accuracy: 0.8968\n",
      "Epoch: 66/160 Train Loss: 0.1389 Accuracy: 0.9518 Time: 5.84091  | Val Loss: 0.3834 Accuracy: 0.8900\n",
      "Epoch: 67/160 Train Loss: 0.1375 Accuracy: 0.9518 Time: 5.94238  | Val Loss: 0.3249 Accuracy: 0.9056\n",
      "Epoch: 68/160 Train Loss: 0.1388 Accuracy: 0.9522 Time: 5.78014  | Val Loss: 0.3213 Accuracy: 0.9070\n",
      "Epoch: 69/160 Train Loss: 0.1349 Accuracy: 0.9533 Time: 5.96304  | Val Loss: 0.3413 Accuracy: 0.8994\n",
      "Epoch: 70/160 Train Loss: 0.1388 Accuracy: 0.9520 Time: 5.86725  | Val Loss: 0.3501 Accuracy: 0.8984\n",
      "Epoch: 71/160 Train Loss: 0.1386 Accuracy: 0.9511 Time: 5.93062  | Val Loss: 0.3385 Accuracy: 0.9017\n",
      "Epoch: 72/160 Train Loss: 0.1417 Accuracy: 0.9507 Time: 5.88047  | Val Loss: 0.3923 Accuracy: 0.8905\n",
      "Epoch: 73/160 Train Loss: 0.1440 Accuracy: 0.9505 Time: 5.74104  | Val Loss: 0.3655 Accuracy: 0.8944\n",
      "Epoch: 74/160 Train Loss: 0.1409 Accuracy: 0.9502 Time: 5.71698  | Val Loss: 0.4216 Accuracy: 0.8818\n",
      "Epoch: 75/160 Train Loss: 0.1407 Accuracy: 0.9519 Time: 5.89776  | Val Loss: 0.3720 Accuracy: 0.8915\n",
      "Epoch: 76/160 Train Loss: 0.1428 Accuracy: 0.9499 Time: 5.69095  | Val Loss: 0.3647 Accuracy: 0.8934\n",
      "Epoch: 77/160 Train Loss: 0.1427 Accuracy: 0.9498 Time: 5.72995  | Val Loss: 0.3434 Accuracy: 0.9026\n",
      "Epoch: 78/160 Train Loss: 0.1403 Accuracy: 0.9509 Time: 5.86980  | Val Loss: 0.3177 Accuracy: 0.9035\n",
      "Epoch: 79/160 Train Loss: 0.1440 Accuracy: 0.9500 Time: 5.75772  | Val Loss: 0.3456 Accuracy: 0.8985\n",
      "Epoch: 80/160 Train Loss: 0.1386 Accuracy: 0.9520 Time: 5.85563  | Val Loss: 0.3458 Accuracy: 0.9008\n",
      "Epoch: 81/160 Train Loss: 0.0852 Accuracy: 0.9719 Time: 5.86277  | Val Loss: 0.2665 Accuracy: 0.9218\n",
      "Epoch: 82/160 Train Loss: 0.0628 Accuracy: 0.9794 Time: 5.78091  | Val Loss: 0.2640 Accuracy: 0.9238\n",
      "Epoch: 83/160 Train Loss: 0.0514 Accuracy: 0.9831 Time: 5.86515  | Val Loss: 0.2761 Accuracy: 0.9219\n",
      "Epoch: 84/160 Train Loss: 0.0474 Accuracy: 0.9842 Time: 5.84205  | Val Loss: 0.2834 Accuracy: 0.9229\n",
      "Epoch: 85/160 Train Loss: 0.0437 Accuracy: 0.9854 Time: 5.71582  | Val Loss: 0.2824 Accuracy: 0.9251\n",
      "Epoch: 86/160 Train Loss: 0.0419 Accuracy: 0.9865 Time: 5.88970  | Val Loss: 0.2847 Accuracy: 0.9265\n",
      "Epoch: 87/160 Train Loss: 0.0393 Accuracy: 0.9869 Time: 5.87976  | Val Loss: 0.2892 Accuracy: 0.9254\n",
      "Epoch: 88/160 Train Loss: 0.0353 Accuracy: 0.9885 Time: 5.75291  | Val Loss: 0.2949 Accuracy: 0.9253\n",
      "Epoch: 89/160 Train Loss: 0.0362 Accuracy: 0.9879 Time: 5.70605  | Val Loss: 0.2996 Accuracy: 0.9255\n",
      "Epoch: 90/160 Train Loss: 0.0310 Accuracy: 0.9895 Time: 5.73230  | Val Loss: 0.3013 Accuracy: 0.9259\n",
      "Epoch: 91/160 Train Loss: 0.0296 Accuracy: 0.9900 Time: 5.79229  | Val Loss: 0.2997 Accuracy: 0.9267\n",
      "Epoch: 92/160 Train Loss: 0.0305 Accuracy: 0.9895 Time: 5.82881  | Val Loss: 0.3087 Accuracy: 0.9264\n",
      "Epoch: 93/160 Train Loss: 0.0260 Accuracy: 0.9915 Time: 5.93297  | Val Loss: 0.3146 Accuracy: 0.9247\n",
      "Epoch: 94/160 Train Loss: 0.0247 Accuracy: 0.9921 Time: 5.84089  | Val Loss: 0.3211 Accuracy: 0.9253\n",
      "Epoch: 95/160 Train Loss: 0.0247 Accuracy: 0.9914 Time: 5.83677  | Val Loss: 0.3287 Accuracy: 0.9255\n",
      "Epoch: 96/160 Train Loss: 0.0254 Accuracy: 0.9915 Time: 5.92248  | Val Loss: 0.3221 Accuracy: 0.9271\n",
      "Epoch: 97/160 Train Loss: 0.0240 Accuracy: 0.9921 Time: 5.77436  | Val Loss: 0.3386 Accuracy: 0.9249\n",
      "Epoch: 98/160 Train Loss: 0.0231 Accuracy: 0.9923 Time: 5.92268  | Val Loss: 0.3352 Accuracy: 0.9255\n",
      "Epoch: 99/160 Train Loss: 0.0232 Accuracy: 0.9924 Time: 5.73865  | Val Loss: 0.3369 Accuracy: 0.9257\n",
      "Epoch: 100/160 Train Loss: 0.0208 Accuracy: 0.9928 Time: 5.82853  | Val Loss: 0.3300 Accuracy: 0.9278\n",
      "Epoch: 101/160 Train Loss: 0.0211 Accuracy: 0.9932 Time: 5.77961  | Val Loss: 0.3382 Accuracy: 0.9263\n",
      "Epoch: 102/160 Train Loss: 0.0210 Accuracy: 0.9930 Time: 5.87828  | Val Loss: 0.3500 Accuracy: 0.9243\n",
      "Epoch: 103/160 Train Loss: 0.0211 Accuracy: 0.9932 Time: 5.85788  | Val Loss: 0.3297 Accuracy: 0.9260\n",
      "Epoch: 104/160 Train Loss: 0.0184 Accuracy: 0.9936 Time: 5.98145  | Val Loss: 0.3328 Accuracy: 0.9252\n",
      "Epoch: 105/160 Train Loss: 0.0185 Accuracy: 0.9938 Time: 5.87486  | Val Loss: 0.3386 Accuracy: 0.9268\n",
      "Epoch: 106/160 Train Loss: 0.0186 Accuracy: 0.9940 Time: 5.76189  | Val Loss: 0.3382 Accuracy: 0.9274\n",
      "Epoch: 107/160 Train Loss: 0.0184 Accuracy: 0.9938 Time: 5.77364  | Val Loss: 0.3466 Accuracy: 0.9264\n",
      "Epoch: 108/160 Train Loss: 0.0186 Accuracy: 0.9939 Time: 5.90779  | Val Loss: 0.3481 Accuracy: 0.9280\n",
      "Epoch: 109/160 Train Loss: 0.0171 Accuracy: 0.9938 Time: 5.83672  | Val Loss: 0.3476 Accuracy: 0.9268\n",
      "Epoch: 110/160 Train Loss: 0.0172 Accuracy: 0.9943 Time: 5.80317  | Val Loss: 0.3523 Accuracy: 0.9270\n",
      "Epoch: 111/160 Train Loss: 0.0172 Accuracy: 0.9941 Time: 5.85447  | Val Loss: 0.3546 Accuracy: 0.9271\n",
      "Epoch: 112/160 Train Loss: 0.0162 Accuracy: 0.9946 Time: 5.92657  | Val Loss: 0.3501 Accuracy: 0.9257\n",
      "Epoch: 113/160 Train Loss: 0.0170 Accuracy: 0.9946 Time: 5.89183  | Val Loss: 0.3565 Accuracy: 0.9261\n",
      "Epoch: 114/160 Train Loss: 0.0156 Accuracy: 0.9950 Time: 5.74122  | Val Loss: 0.3543 Accuracy: 0.9267\n",
      "Epoch: 115/160 Train Loss: 0.0160 Accuracy: 0.9945 Time: 5.70160  | Val Loss: 0.3531 Accuracy: 0.9270\n",
      "Epoch: 116/160 Train Loss: 0.0160 Accuracy: 0.9949 Time: 5.75966  | Val Loss: 0.3562 Accuracy: 0.9239\n",
      "Epoch: 117/160 Train Loss: 0.0157 Accuracy: 0.9948 Time: 5.77890  | Val Loss: 0.3579 Accuracy: 0.9270\n",
      "Epoch: 118/160 Train Loss: 0.0153 Accuracy: 0.9948 Time: 5.85533  | Val Loss: 0.3590 Accuracy: 0.9273\n",
      "Epoch: 119/160 Train Loss: 0.0142 Accuracy: 0.9956 Time: 5.76546  | Val Loss: 0.3608 Accuracy: 0.9265\n",
      "Epoch: 120/160 Train Loss: 0.0150 Accuracy: 0.9951 Time: 5.85471  | Val Loss: 0.3595 Accuracy: 0.9251\n",
      "Epoch: 121/160 Train Loss: 0.0122 Accuracy: 0.9960 Time: 5.82701  | Val Loss: 0.3542 Accuracy: 0.9275\n",
      "Epoch: 122/160 Train Loss: 0.0115 Accuracy: 0.9963 Time: 5.77258  | Val Loss: 0.3516 Accuracy: 0.9276\n",
      "Epoch: 123/160 Train Loss: 0.0101 Accuracy: 0.9970 Time: 5.71524  | Val Loss: 0.3507 Accuracy: 0.9277\n",
      "Epoch: 124/160 Train Loss: 0.0112 Accuracy: 0.9964 Time: 5.76374  | Val Loss: 0.3472 Accuracy: 0.9293\n",
      "Epoch: 125/160 Train Loss: 0.0099 Accuracy: 0.9971 Time: 5.80219  | Val Loss: 0.3502 Accuracy: 0.9287\n",
      "Epoch: 126/160 Train Loss: 0.0097 Accuracy: 0.9968 Time: 5.78119  | Val Loss: 0.3489 Accuracy: 0.9293\n",
      "Epoch: 127/160 Train Loss: 0.0085 Accuracy: 0.9972 Time: 5.67482  | Val Loss: 0.3532 Accuracy: 0.9286\n",
      "Epoch: 128/160 Train Loss: 0.0088 Accuracy: 0.9970 Time: 5.70364  | Val Loss: 0.3539 Accuracy: 0.9295\n",
      "Epoch: 129/160 Train Loss: 0.0092 Accuracy: 0.9970 Time: 5.89823  | Val Loss: 0.3515 Accuracy: 0.9294\n",
      "Epoch: 130/160 Train Loss: 0.0085 Accuracy: 0.9974 Time: 5.74006  | Val Loss: 0.3524 Accuracy: 0.9292\n",
      "Epoch: 131/160 Train Loss: 0.0088 Accuracy: 0.9975 Time: 5.87218  | Val Loss: 0.3522 Accuracy: 0.9292\n",
      "Epoch: 132/160 Train Loss: 0.0086 Accuracy: 0.9976 Time: 5.72676  | Val Loss: 0.3511 Accuracy: 0.9292\n",
      "Epoch: 133/160 Train Loss: 0.0084 Accuracy: 0.9974 Time: 5.83446  | Val Loss: 0.3530 Accuracy: 0.9291\n",
      "Epoch: 134/160 Train Loss: 0.0077 Accuracy: 0.9977 Time: 5.72568  | Val Loss: 0.3516 Accuracy: 0.9296\n",
      "Epoch: 135/160 Train Loss: 0.0081 Accuracy: 0.9976 Time: 5.71865  | Val Loss: 0.3536 Accuracy: 0.9292\n",
      "Epoch: 136/160 Train Loss: 0.0082 Accuracy: 0.9973 Time: 5.72571  | Val Loss: 0.3538 Accuracy: 0.9295\n",
      "Epoch: 137/160 Train Loss: 0.0088 Accuracy: 0.9974 Time: 5.85463  | Val Loss: 0.3571 Accuracy: 0.9294\n",
      "Epoch: 138/160 Train Loss: 0.0085 Accuracy: 0.9974 Time: 5.83662  | Val Loss: 0.3565 Accuracy: 0.9298\n",
      "Epoch: 139/160 Train Loss: 0.0081 Accuracy: 0.9977 Time: 5.89403  | Val Loss: 0.3558 Accuracy: 0.9286\n",
      "Epoch: 140/160 Train Loss: 0.0074 Accuracy: 0.9978 Time: 5.71469  | Val Loss: 0.3553 Accuracy: 0.9296\n",
      "Epoch: 141/160 Train Loss: 0.0076 Accuracy: 0.9978 Time: 5.76090  | Val Loss: 0.3550 Accuracy: 0.9297\n",
      "Epoch: 142/160 Train Loss: 0.0076 Accuracy: 0.9977 Time: 5.77952  | Val Loss: 0.3549 Accuracy: 0.9306\n",
      "Epoch: 143/160 Train Loss: 0.0080 Accuracy: 0.9975 Time: 5.82683  | Val Loss: 0.3564 Accuracy: 0.9285\n",
      "Epoch: 144/160 Train Loss: 0.0069 Accuracy: 0.9980 Time: 5.82066  | Val Loss: 0.3538 Accuracy: 0.9283\n",
      "Epoch: 145/160 Train Loss: 0.0077 Accuracy: 0.9974 Time: 5.84865  | Val Loss: 0.3567 Accuracy: 0.9285\n",
      "Epoch: 146/160 Train Loss: 0.0075 Accuracy: 0.9976 Time: 5.91244  | Val Loss: 0.3552 Accuracy: 0.9298\n",
      "Epoch: 147/160 Train Loss: 0.0072 Accuracy: 0.9980 Time: 5.89781  | Val Loss: 0.3550 Accuracy: 0.9298\n",
      "Epoch: 148/160 Train Loss: 0.0076 Accuracy: 0.9976 Time: 5.92265  | Val Loss: 0.3582 Accuracy: 0.9304\n",
      "Epoch: 149/160 Train Loss: 0.0075 Accuracy: 0.9977 Time: 5.73125  | Val Loss: 0.3569 Accuracy: 0.9300\n",
      "Epoch: 150/160 Train Loss: 0.0079 Accuracy: 0.9977 Time: 5.74516  | Val Loss: 0.3558 Accuracy: 0.9301\n",
      "Epoch: 151/160 Train Loss: 0.0072 Accuracy: 0.9980 Time: 5.81040  | Val Loss: 0.3532 Accuracy: 0.9300\n",
      "Epoch: 152/160 Train Loss: 0.0070 Accuracy: 0.9980 Time: 5.83568  | Val Loss: 0.3533 Accuracy: 0.9292\n",
      "Epoch: 153/160 Train Loss: 0.0079 Accuracy: 0.9976 Time: 5.91179  | Val Loss: 0.3547 Accuracy: 0.9302\n",
      "Epoch: 154/160 Train Loss: 0.0071 Accuracy: 0.9977 Time: 5.82004  | Val Loss: 0.3543 Accuracy: 0.9305\n",
      "Epoch: 155/160 Train Loss: 0.0073 Accuracy: 0.9979 Time: 5.86349  | Val Loss: 0.3552 Accuracy: 0.9296\n",
      "Epoch: 156/160 Train Loss: 0.0082 Accuracy: 0.9977 Time: 5.82305  | Val Loss: 0.3556 Accuracy: 0.9295\n",
      "Epoch: 157/160 Train Loss: 0.0069 Accuracy: 0.9980 Time: 5.70731  | Val Loss: 0.3579 Accuracy: 0.9306\n",
      "Epoch: 158/160 Train Loss: 0.0073 Accuracy: 0.9977 Time: 5.75902  | Val Loss: 0.3561 Accuracy: 0.9306\n",
      "Epoch: 159/160 Train Loss: 0.0070 Accuracy: 0.9978 Time: 5.88124  | Val Loss: 0.3584 Accuracy: 0.9301\n",
      "Epoch: 160/160 Train Loss: 0.0070 Accuracy: 0.9978 Time: 5.85359  | Val Loss: 0.3572 Accuracy: 0.9300\n",
      "#Parameter: 11173962 Accuracy: 0.9298\n"
     ]
    }
   ],
   "source": [
    "from spacy import training\n",
    "from hdd.models.cnn.resnet import ResnetSmall, resnet18_config\n",
    "from hdd.train.early_stopping import EarlyStoppingInMem\n",
    "from hdd.train.classification_utils import (\n",
    "    naive_train_classification_model,\n",
    "    eval_image_classifier,\n",
    ")\n",
    "from hdd.models.nn_utils import count_trainable_parameter\n",
    "\n",
    "\n",
    "def train_net(\n",
    "    resnet_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout,\n",
    "    lr,\n",
    "    weight_decay,\n",
    "    step_size=40,\n",
    "    gamma=0.1,\n",
    "    max_epochs=160,\n",
    ") -> tuple[ResnetSmall, dict[str, list[float]]]:\n",
    "    net = ResnetSmall(resnet_config, num_classes=10, dropout=dropout).to(DEVICE)\n",
    "    criteria = nn.CrossEntropyLoss()\n",
    "    # SGD的收敛速度远不如Adam好\n",
    "    # optimizer = torch.optim.SGD(\n",
    "    #     net.parameters(), lr=lr, momentum=0.9, weight_decay=weight_decay\n",
    "    # )\n",
    "    optimizer = optim.AdamW(\n",
    "        net.parameters(), lr=lr, eps=1e-6, weight_decay=weight_decay\n",
    "    )\n",
    "    scheduler = torch.optim.lr_scheduler.StepLR(\n",
    "        optimizer, step_size=step_size, gamma=gamma, last_epoch=-1\n",
    "    )\n",
    "    training_stats = naive_train_classification_model(\n",
    "        net,\n",
    "        criteria,\n",
    "        max_epochs,\n",
    "        train_dataloader,\n",
    "        val_dataloader,\n",
    "        DEVICE,\n",
    "        optimizer,\n",
    "        scheduler,\n",
    "        verbose=True,\n",
    "    )\n",
    "    return net, training_stats\n",
    "\n",
    "\n",
    "net, resnet18_stats = train_net(\n",
    "    resnet18_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout=0.5,\n",
    "    lr=0.01,\n",
    "    weight_decay=1e-1,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59f07e9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/160 Train Loss: 1.5622 Accuracy: 0.4153 Time: 10.37857  | Val Loss: 1.4469 Accuracy: 0.5166\n",
      "Epoch: 2/160 Train Loss: 1.1085 Accuracy: 0.6014 Time: 10.18682  | Val Loss: 1.3927 Accuracy: 0.5787\n",
      "Epoch: 3/160 Train Loss: 0.8976 Accuracy: 0.6871 Time: 10.15149  | Val Loss: 1.0222 Accuracy: 0.6488\n",
      "Epoch: 4/160 Train Loss: 0.7818 Accuracy: 0.7330 Time: 10.16723  | Val Loss: 0.9724 Accuracy: 0.6693\n",
      "Epoch: 5/160 Train Loss: 0.7187 Accuracy: 0.7550 Time: 10.14769  | Val Loss: 1.0867 Accuracy: 0.6745\n",
      "Epoch: 6/160 Train Loss: 0.6774 Accuracy: 0.7711 Time: 9.96003  | Val Loss: 0.6551 Accuracy: 0.7790\n",
      "Epoch: 7/160 Train Loss: 0.6473 Accuracy: 0.7828 Time: 10.01736  | Val Loss: 0.7636 Accuracy: 0.7374\n",
      "Epoch: 8/160 Train Loss: 0.6317 Accuracy: 0.7858 Time: 10.15978  | Val Loss: 0.7019 Accuracy: 0.7632\n",
      "Epoch: 9/160 Train Loss: 0.6115 Accuracy: 0.7945 Time: 10.12010  | Val Loss: 0.7395 Accuracy: 0.7529\n",
      "Epoch: 10/160 Train Loss: 0.6129 Accuracy: 0.7943 Time: 9.94475  | Val Loss: 0.9997 Accuracy: 0.6907\n",
      "Epoch: 11/160 Train Loss: 0.5917 Accuracy: 0.8011 Time: 10.28887  | Val Loss: 0.7937 Accuracy: 0.7317\n",
      "Epoch: 12/160 Train Loss: 0.5865 Accuracy: 0.8024 Time: 10.45186  | Val Loss: 1.1188 Accuracy: 0.6985\n",
      "Epoch: 13/160 Train Loss: 0.5819 Accuracy: 0.8046 Time: 9.92818  | Val Loss: 0.7884 Accuracy: 0.7402\n",
      "Epoch: 14/160 Train Loss: 0.5777 Accuracy: 0.8067 Time: 10.16899  | Val Loss: 0.6730 Accuracy: 0.7903\n",
      "Epoch: 15/160 Train Loss: 0.5706 Accuracy: 0.8066 Time: 9.97643  | Val Loss: 1.0276 Accuracy: 0.6658\n",
      "Epoch: 16/160 Train Loss: 0.5719 Accuracy: 0.8071 Time: 10.21162  | Val Loss: 0.8408 Accuracy: 0.7223\n",
      "Epoch: 17/160 Train Loss: 0.5705 Accuracy: 0.8100 Time: 10.32851  | Val Loss: 1.0562 Accuracy: 0.6989\n",
      "Epoch: 18/160 Train Loss: 0.5615 Accuracy: 0.8107 Time: 10.18514  | Val Loss: 0.8048 Accuracy: 0.7522\n",
      "Epoch: 19/160 Train Loss: 0.5649 Accuracy: 0.8119 Time: 10.15651  | Val Loss: 0.9198 Accuracy: 0.7237\n",
      "Epoch: 20/160 Train Loss: 0.5640 Accuracy: 0.8119 Time: 10.22554  | Val Loss: 0.7032 Accuracy: 0.7745\n",
      "Epoch: 21/160 Train Loss: 0.5635 Accuracy: 0.8094 Time: 9.99337  | Val Loss: 0.9084 Accuracy: 0.7252\n",
      "Epoch: 22/160 Train Loss: 0.5625 Accuracy: 0.8141 Time: 9.98178  | Val Loss: 0.9730 Accuracy: 0.6921\n",
      "Epoch: 23/160 Train Loss: 0.5571 Accuracy: 0.8153 Time: 10.34992  | Val Loss: 0.9364 Accuracy: 0.7114\n",
      "Epoch: 24/160 Train Loss: 0.5601 Accuracy: 0.8110 Time: 10.09785  | Val Loss: 0.9795 Accuracy: 0.7012\n",
      "Epoch: 25/160 Train Loss: 0.5653 Accuracy: 0.8095 Time: 10.03306  | Val Loss: 0.6945 Accuracy: 0.7674\n",
      "Epoch: 26/160 Train Loss: 0.5616 Accuracy: 0.8121 Time: 10.13730  | Val Loss: 1.0930 Accuracy: 0.6778\n",
      "Epoch: 27/160 Train Loss: 0.5616 Accuracy: 0.8109 Time: 10.01519  | Val Loss: 0.7407 Accuracy: 0.7648\n",
      "Epoch: 28/160 Train Loss: 0.5557 Accuracy: 0.8124 Time: 10.00019  | Val Loss: 0.8481 Accuracy: 0.7239\n",
      "Epoch: 29/160 Train Loss: 0.5563 Accuracy: 0.8139 Time: 10.14052  | Val Loss: 0.8640 Accuracy: 0.7467\n",
      "Epoch: 30/160 Train Loss: 0.5554 Accuracy: 0.8119 Time: 10.18643  | Val Loss: 1.1499 Accuracy: 0.6583\n",
      "Epoch: 31/160 Train Loss: 0.5596 Accuracy: 0.8144 Time: 9.99024  | Val Loss: 0.8746 Accuracy: 0.7248\n",
      "Epoch: 32/160 Train Loss: 0.5597 Accuracy: 0.8127 Time: 9.92800  | Val Loss: 0.5355 Accuracy: 0.8201\n",
      "Epoch: 33/160 Train Loss: 0.5572 Accuracy: 0.8122 Time: 10.20415  | Val Loss: 0.6817 Accuracy: 0.7760\n",
      "Epoch: 34/160 Train Loss: 0.5552 Accuracy: 0.8131 Time: 10.01579  | Val Loss: 0.8421 Accuracy: 0.7389\n",
      "Epoch: 35/160 Train Loss: 0.5598 Accuracy: 0.8130 Time: 9.97586  | Val Loss: 0.6361 Accuracy: 0.7924\n",
      "Epoch: 36/160 Train Loss: 0.5555 Accuracy: 0.8146 Time: 10.24123  | Val Loss: 0.8363 Accuracy: 0.7384\n",
      "Epoch: 37/160 Train Loss: 0.5529 Accuracy: 0.8147 Time: 10.15641  | Val Loss: 0.7178 Accuracy: 0.7606\n",
      "Epoch: 38/160 Train Loss: 0.5566 Accuracy: 0.8131 Time: 10.41920  | Val Loss: 3.1259 Accuracy: 0.4672\n",
      "Epoch: 39/160 Train Loss: 0.5531 Accuracy: 0.8152 Time: 10.03965  | Val Loss: 0.7934 Accuracy: 0.7448\n",
      "Epoch: 40/160 Train Loss: 0.5519 Accuracy: 0.8154 Time: 10.20058  | Val Loss: 0.6600 Accuracy: 0.7844\n",
      "Epoch: 41/160 Train Loss: 0.3394 Accuracy: 0.8865 Time: 10.19092  | Val Loss: 0.3107 Accuracy: 0.8935\n",
      "Epoch: 42/160 Train Loss: 0.2728 Accuracy: 0.9080 Time: 10.11477  | Val Loss: 0.2881 Accuracy: 0.9017\n",
      "Epoch: 43/160 Train Loss: 0.2441 Accuracy: 0.9165 Time: 9.98034  | Val Loss: 0.2846 Accuracy: 0.9041\n",
      "Epoch: 44/160 Train Loss: 0.2260 Accuracy: 0.9231 Time: 10.22152  | Val Loss: 0.2765 Accuracy: 0.9080\n",
      "Epoch: 45/160 Train Loss: 0.2149 Accuracy: 0.9276 Time: 10.08192  | Val Loss: 0.2795 Accuracy: 0.9088\n",
      "Epoch: 46/160 Train Loss: 0.1971 Accuracy: 0.9331 Time: 10.31175  | Val Loss: 0.2802 Accuracy: 0.9108\n",
      "Epoch: 47/160 Train Loss: 0.1875 Accuracy: 0.9361 Time: 9.89982  | Val Loss: 0.2804 Accuracy: 0.9121\n",
      "Epoch: 48/160 Train Loss: 0.1730 Accuracy: 0.9408 Time: 10.00488  | Val Loss: 0.2865 Accuracy: 0.9115\n",
      "Epoch: 49/160 Train Loss: 0.1657 Accuracy: 0.9430 Time: 10.33864  | Val Loss: 0.2898 Accuracy: 0.9089\n",
      "Epoch: 50/160 Train Loss: 0.1558 Accuracy: 0.9462 Time: 10.24274  | Val Loss: 0.3094 Accuracy: 0.9061\n",
      "Epoch: 51/160 Train Loss: 0.1493 Accuracy: 0.9483 Time: 9.92435  | Val Loss: 0.3081 Accuracy: 0.9066\n",
      "Epoch: 52/160 Train Loss: 0.1461 Accuracy: 0.9507 Time: 10.29845  | Val Loss: 0.2778 Accuracy: 0.9135\n",
      "Epoch: 53/160 Train Loss: 0.1355 Accuracy: 0.9527 Time: 10.04678  | Val Loss: 0.3074 Accuracy: 0.9092\n",
      "Epoch: 54/160 Train Loss: 0.1370 Accuracy: 0.9530 Time: 10.13831  | Val Loss: 0.2925 Accuracy: 0.9136\n",
      "Epoch: 55/160 Train Loss: 0.1306 Accuracy: 0.9537 Time: 9.97279  | Val Loss: 0.2818 Accuracy: 0.9157\n",
      "Epoch: 56/160 Train Loss: 0.1276 Accuracy: 0.9561 Time: 9.96505  | Val Loss: 0.3092 Accuracy: 0.9092\n",
      "Epoch: 57/160 Train Loss: 0.1261 Accuracy: 0.9565 Time: 10.10345  | Val Loss: 0.3102 Accuracy: 0.9119\n",
      "Epoch: 58/160 Train Loss: 0.1226 Accuracy: 0.9580 Time: 10.10817  | Val Loss: 0.3374 Accuracy: 0.9039\n",
      "Epoch: 59/160 Train Loss: 0.1229 Accuracy: 0.9574 Time: 10.40436  | Val Loss: 0.2901 Accuracy: 0.9147\n",
      "Epoch: 60/160 Train Loss: 0.1196 Accuracy: 0.9589 Time: 10.40305  | Val Loss: 0.3302 Accuracy: 0.9077\n",
      "Epoch: 61/160 Train Loss: 0.1193 Accuracy: 0.9589 Time: 10.12215  | Val Loss: 0.3202 Accuracy: 0.9079\n",
      "Epoch: 62/160 Train Loss: 0.1157 Accuracy: 0.9591 Time: 10.46012  | Val Loss: 0.3147 Accuracy: 0.9111\n",
      "Epoch: 63/160 Train Loss: 0.1211 Accuracy: 0.9581 Time: 10.26389  | Val Loss: 0.3295 Accuracy: 0.9057\n",
      "Epoch: 64/160 Train Loss: 0.1142 Accuracy: 0.9605 Time: 10.20048  | Val Loss: 0.3294 Accuracy: 0.9097\n",
      "Epoch: 65/160 Train Loss: 0.1148 Accuracy: 0.9607 Time: 10.32985  | Val Loss: 0.3440 Accuracy: 0.9051\n",
      "Epoch: 66/160 Train Loss: 0.1173 Accuracy: 0.9594 Time: 9.93776  | Val Loss: 0.4038 Accuracy: 0.8929\n",
      "Epoch: 67/160 Train Loss: 0.1132 Accuracy: 0.9616 Time: 10.13489  | Val Loss: 0.3260 Accuracy: 0.9109\n",
      "Epoch: 68/160 Train Loss: 0.1175 Accuracy: 0.9593 Time: 10.10990  | Val Loss: 0.3205 Accuracy: 0.9092\n",
      "Epoch: 69/160 Train Loss: 0.1183 Accuracy: 0.9596 Time: 10.00284  | Val Loss: 0.3359 Accuracy: 0.9066\n",
      "Epoch: 70/160 Train Loss: 0.1159 Accuracy: 0.9604 Time: 10.24425  | Val Loss: 0.3637 Accuracy: 0.9025\n",
      "Epoch: 71/160 Train Loss: 0.1132 Accuracy: 0.9612 Time: 10.14190  | Val Loss: 0.3434 Accuracy: 0.9041\n",
      "Epoch: 72/160 Train Loss: 0.1170 Accuracy: 0.9601 Time: 10.08042  | Val Loss: 0.3682 Accuracy: 0.9014\n",
      "Epoch: 73/160 Train Loss: 0.1215 Accuracy: 0.9584 Time: 10.10953  | Val Loss: 0.3260 Accuracy: 0.9085\n",
      "Epoch: 74/160 Train Loss: 0.1164 Accuracy: 0.9601 Time: 9.93034  | Val Loss: 0.3361 Accuracy: 0.9063\n",
      "Epoch: 75/160 Train Loss: 0.1173 Accuracy: 0.9597 Time: 10.33844  | Val Loss: 0.3034 Accuracy: 0.9104\n",
      "Epoch: 76/160 Train Loss: 0.1195 Accuracy: 0.9586 Time: 10.16364  | Val Loss: 0.3423 Accuracy: 0.9037\n",
      "Epoch: 77/160 Train Loss: 0.1175 Accuracy: 0.9596 Time: 10.18767  | Val Loss: 0.3642 Accuracy: 0.9008\n",
      "Epoch: 78/160 Train Loss: 0.1196 Accuracy: 0.9594 Time: 10.24669  | Val Loss: 0.3478 Accuracy: 0.8996\n",
      "Epoch: 79/160 Train Loss: 0.1200 Accuracy: 0.9585 Time: 10.29330  | Val Loss: 0.3396 Accuracy: 0.9057\n",
      "Epoch: 80/160 Train Loss: 0.1221 Accuracy: 0.9585 Time: 10.06965  | Val Loss: 0.3460 Accuracy: 0.9066\n",
      "Epoch: 81/160 Train Loss: 0.0660 Accuracy: 0.9782 Time: 9.91360  | Val Loss: 0.2696 Accuracy: 0.9266\n",
      "Epoch: 82/160 Train Loss: 0.0453 Accuracy: 0.9849 Time: 10.21060  | Val Loss: 0.2741 Accuracy: 0.9296\n",
      "Epoch: 83/160 Train Loss: 0.0391 Accuracy: 0.9871 Time: 9.97979  | Val Loss: 0.2866 Accuracy: 0.9296\n",
      "Epoch: 84/160 Train Loss: 0.0325 Accuracy: 0.9900 Time: 9.92577  | Val Loss: 0.2905 Accuracy: 0.9306\n",
      "Epoch: 85/160 Train Loss: 0.0287 Accuracy: 0.9902 Time: 10.12096  | Val Loss: 0.3046 Accuracy: 0.9297\n",
      "Epoch: 86/160 Train Loss: 0.0263 Accuracy: 0.9916 Time: 10.13233  | Val Loss: 0.3012 Accuracy: 0.9312\n",
      "Epoch: 87/160 Train Loss: 0.0247 Accuracy: 0.9922 Time: 9.91111  | Val Loss: 0.3166 Accuracy: 0.9319\n",
      "Epoch: 88/160 Train Loss: 0.0231 Accuracy: 0.9926 Time: 10.00398  | Val Loss: 0.3153 Accuracy: 0.9296\n",
      "Epoch: 89/160 Train Loss: 0.0203 Accuracy: 0.9934 Time: 10.00693  | Val Loss: 0.3342 Accuracy: 0.9296\n",
      "Epoch: 90/160 Train Loss: 0.0198 Accuracy: 0.9935 Time: 10.09530  | Val Loss: 0.3247 Accuracy: 0.9302\n",
      "Epoch: 91/160 Train Loss: 0.0193 Accuracy: 0.9935 Time: 9.98490  | Val Loss: 0.3395 Accuracy: 0.9285\n",
      "Epoch: 92/160 Train Loss: 0.0176 Accuracy: 0.9938 Time: 10.02009  | Val Loss: 0.3522 Accuracy: 0.9291\n",
      "Epoch: 93/160 Train Loss: 0.0163 Accuracy: 0.9947 Time: 10.01193  | Val Loss: 0.3454 Accuracy: 0.9317\n",
      "Epoch: 94/160 Train Loss: 0.0167 Accuracy: 0.9948 Time: 10.49667  | Val Loss: 0.3473 Accuracy: 0.9318\n",
      "Epoch: 95/160 Train Loss: 0.0140 Accuracy: 0.9951 Time: 10.18917  | Val Loss: 0.3526 Accuracy: 0.9316\n",
      "Epoch: 96/160 Train Loss: 0.0142 Accuracy: 0.9955 Time: 10.08405  | Val Loss: 0.3601 Accuracy: 0.9295\n",
      "Epoch: 97/160 Train Loss: 0.0138 Accuracy: 0.9955 Time: 9.95549  | Val Loss: 0.3630 Accuracy: 0.9301\n",
      "Epoch: 98/160 Train Loss: 0.0143 Accuracy: 0.9953 Time: 10.03490  | Val Loss: 0.3730 Accuracy: 0.9300\n",
      "Epoch: 99/160 Train Loss: 0.0149 Accuracy: 0.9952 Time: 10.02642  | Val Loss: 0.3642 Accuracy: 0.9301\n",
      "Epoch: 100/160 Train Loss: 0.0146 Accuracy: 0.9951 Time: 10.06538  | Val Loss: 0.3682 Accuracy: 0.9296\n",
      "Epoch: 101/160 Train Loss: 0.0115 Accuracy: 0.9961 Time: 10.28852  | Val Loss: 0.3785 Accuracy: 0.9294\n",
      "Epoch: 102/160 Train Loss: 0.0129 Accuracy: 0.9958 Time: 10.18547  | Val Loss: 0.3855 Accuracy: 0.9292\n",
      "Epoch: 103/160 Train Loss: 0.0136 Accuracy: 0.9954 Time: 9.98305  | Val Loss: 0.3771 Accuracy: 0.9310\n",
      "Epoch: 104/160 Train Loss: 0.0120 Accuracy: 0.9961 Time: 9.97670  | Val Loss: 0.3732 Accuracy: 0.9316\n",
      "Epoch: 105/160 Train Loss: 0.0112 Accuracy: 0.9966 Time: 9.94644  | Val Loss: 0.3753 Accuracy: 0.9320\n",
      "Epoch: 106/160 Train Loss: 0.0112 Accuracy: 0.9960 Time: 9.93108  | Val Loss: 0.3795 Accuracy: 0.9293\n",
      "Epoch: 107/160 Train Loss: 0.0115 Accuracy: 0.9960 Time: 10.25441  | Val Loss: 0.3861 Accuracy: 0.9299\n",
      "Epoch: 108/160 Train Loss: 0.0107 Accuracy: 0.9965 Time: 10.22646  | Val Loss: 0.3840 Accuracy: 0.9307\n",
      "Epoch: 109/160 Train Loss: 0.0107 Accuracy: 0.9962 Time: 10.52512  | Val Loss: 0.3804 Accuracy: 0.9298\n",
      "Epoch: 110/160 Train Loss: 0.0101 Accuracy: 0.9966 Time: 10.82218  | Val Loss: 0.3985 Accuracy: 0.9296\n",
      "Epoch: 111/160 Train Loss: 0.0101 Accuracy: 0.9965 Time: 10.63154  | Val Loss: 0.3891 Accuracy: 0.9295\n",
      "Epoch: 112/160 Train Loss: 0.0114 Accuracy: 0.9962 Time: 10.63024  | Val Loss: 0.3916 Accuracy: 0.9318\n",
      "Epoch: 113/160 Train Loss: 0.0111 Accuracy: 0.9964 Time: 10.65513  | Val Loss: 0.3773 Accuracy: 0.9294\n",
      "Epoch: 114/160 Train Loss: 0.0098 Accuracy: 0.9969 Time: 10.70669  | Val Loss: 0.3945 Accuracy: 0.9306\n",
      "Epoch: 115/160 Train Loss: 0.0106 Accuracy: 0.9966 Time: 10.65617  | Val Loss: 0.3922 Accuracy: 0.9294\n",
      "Epoch: 116/160 Train Loss: 0.0103 Accuracy: 0.9965 Time: 10.51614  | Val Loss: 0.3956 Accuracy: 0.9297\n",
      "Epoch: 117/160 Train Loss: 0.0096 Accuracy: 0.9966 Time: 10.52761  | Val Loss: 0.3946 Accuracy: 0.9296\n",
      "Epoch: 118/160 Train Loss: 0.0095 Accuracy: 0.9969 Time: 10.64075  | Val Loss: 0.3920 Accuracy: 0.9309\n",
      "Epoch: 119/160 Train Loss: 0.0092 Accuracy: 0.9970 Time: 10.76674  | Val Loss: 0.3816 Accuracy: 0.9306\n",
      "Epoch: 120/160 Train Loss: 0.0100 Accuracy: 0.9967 Time: 10.74927  | Val Loss: 0.3815 Accuracy: 0.9323\n",
      "Epoch: 121/160 Train Loss: 0.0070 Accuracy: 0.9980 Time: 10.78501  | Val Loss: 0.3782 Accuracy: 0.9343\n",
      "Epoch: 122/160 Train Loss: 0.0083 Accuracy: 0.9973 Time: 10.95726  | Val Loss: 0.3757 Accuracy: 0.9336\n",
      "Epoch: 123/160 Train Loss: 0.0062 Accuracy: 0.9979 Time: 10.65638  | Val Loss: 0.3699 Accuracy: 0.9338\n",
      "Epoch: 124/160 Train Loss: 0.0059 Accuracy: 0.9981 Time: 10.64502  | Val Loss: 0.3740 Accuracy: 0.9334\n",
      "Epoch: 125/160 Train Loss: 0.0057 Accuracy: 0.9981 Time: 10.76774  | Val Loss: 0.3704 Accuracy: 0.9346\n",
      "Epoch: 126/160 Train Loss: 0.0053 Accuracy: 0.9984 Time: 10.56419  | Val Loss: 0.3756 Accuracy: 0.9345\n",
      "Epoch: 127/160 Train Loss: 0.0053 Accuracy: 0.9983 Time: 10.56411  | Val Loss: 0.3752 Accuracy: 0.9344\n",
      "Epoch: 128/160 Train Loss: 0.0057 Accuracy: 0.9983 Time: 10.57534  | Val Loss: 0.3758 Accuracy: 0.9348\n",
      "Epoch: 129/160 Train Loss: 0.0047 Accuracy: 0.9986 Time: 10.60775  | Val Loss: 0.3742 Accuracy: 0.9345\n",
      "Epoch: 130/160 Train Loss: 0.0053 Accuracy: 0.9983 Time: 10.56242  | Val Loss: 0.3745 Accuracy: 0.9348\n",
      "Epoch: 131/160 Train Loss: 0.0058 Accuracy: 0.9980 Time: 10.58862  | Val Loss: 0.3761 Accuracy: 0.9350\n",
      "Epoch: 132/160 Train Loss: 0.0048 Accuracy: 0.9986 Time: 10.74341  | Val Loss: 0.3797 Accuracy: 0.9337\n",
      "Epoch: 133/160 Train Loss: 0.0045 Accuracy: 0.9988 Time: 10.59630  | Val Loss: 0.3789 Accuracy: 0.9341\n",
      "Epoch: 134/160 Train Loss: 0.0049 Accuracy: 0.9986 Time: 10.85264  | Val Loss: 0.3844 Accuracy: 0.9343\n",
      "Epoch: 135/160 Train Loss: 0.0051 Accuracy: 0.9984 Time: 10.74421  | Val Loss: 0.3829 Accuracy: 0.9331\n",
      "Epoch: 136/160 Train Loss: 0.0043 Accuracy: 0.9988 Time: 10.63376  | Val Loss: 0.3858 Accuracy: 0.9342\n",
      "Epoch: 137/160 Train Loss: 0.0048 Accuracy: 0.9986 Time: 10.66454  | Val Loss: 0.3853 Accuracy: 0.9346\n",
      "Epoch: 138/160 Train Loss: 0.0039 Accuracy: 0.9988 Time: 10.64423  | Val Loss: 0.3837 Accuracy: 0.9340\n",
      "Epoch: 139/160 Train Loss: 0.0041 Accuracy: 0.9986 Time: 10.05839  | Val Loss: 0.3868 Accuracy: 0.9340\n",
      "Epoch: 140/160 Train Loss: 0.0043 Accuracy: 0.9985 Time: 10.25070  | Val Loss: 0.3878 Accuracy: 0.9336\n",
      "Epoch: 141/160 Train Loss: 0.0044 Accuracy: 0.9987 Time: 10.22793  | Val Loss: 0.3854 Accuracy: 0.9351\n",
      "Epoch: 142/160 Train Loss: 0.0047 Accuracy: 0.9986 Time: 10.20594  | Val Loss: 0.3897 Accuracy: 0.9345\n",
      "Epoch: 143/160 Train Loss: 0.0036 Accuracy: 0.9990 Time: 10.20399  | Val Loss: 0.3885 Accuracy: 0.9347\n",
      "Epoch: 144/160 Train Loss: 0.0044 Accuracy: 0.9988 Time: 10.34681  | Val Loss: 0.3878 Accuracy: 0.9352\n",
      "Epoch: 145/160 Train Loss: 0.0038 Accuracy: 0.9988 Time: 10.30238  | Val Loss: 0.3900 Accuracy: 0.9344\n",
      "Epoch: 146/160 Train Loss: 0.0039 Accuracy: 0.9988 Time: 10.17042  | Val Loss: 0.3890 Accuracy: 0.9354\n",
      "Epoch: 147/160 Train Loss: 0.0038 Accuracy: 0.9988 Time: 10.03443  | Val Loss: 0.3897 Accuracy: 0.9350\n",
      "Epoch: 148/160 Train Loss: 0.0040 Accuracy: 0.9987 Time: 10.55666  | Val Loss: 0.3882 Accuracy: 0.9350\n",
      "Epoch: 149/160 Train Loss: 0.0041 Accuracy: 0.9987 Time: 10.45661  | Val Loss: 0.3906 Accuracy: 0.9353\n",
      "Epoch: 150/160 Train Loss: 0.0041 Accuracy: 0.9989 Time: 10.55724  | Val Loss: 0.3990 Accuracy: 0.9349\n",
      "Epoch: 151/160 Train Loss: 0.0037 Accuracy: 0.9989 Time: 10.16994  | Val Loss: 0.3944 Accuracy: 0.9360\n",
      "Epoch: 152/160 Train Loss: 0.0040 Accuracy: 0.9987 Time: 10.07785  | Val Loss: 0.3935 Accuracy: 0.9340\n",
      "Epoch: 153/160 Train Loss: 0.0039 Accuracy: 0.9987 Time: 9.91541  | Val Loss: 0.3928 Accuracy: 0.9354\n",
      "Epoch: 154/160 Train Loss: 0.0034 Accuracy: 0.9991 Time: 9.94423  | Val Loss: 0.3942 Accuracy: 0.9343\n",
      "Epoch: 155/160 Train Loss: 0.0040 Accuracy: 0.9988 Time: 10.15226  | Val Loss: 0.3914 Accuracy: 0.9350\n",
      "Epoch: 156/160 Train Loss: 0.0039 Accuracy: 0.9990 Time: 10.04340  | Val Loss: 0.3951 Accuracy: 0.9361\n",
      "Epoch: 157/160 Train Loss: 0.0040 Accuracy: 0.9989 Time: 9.92691  | Val Loss: 0.3955 Accuracy: 0.9355\n",
      "Epoch: 158/160 Train Loss: 0.0036 Accuracy: 0.9989 Time: 10.47756  | Val Loss: 0.4011 Accuracy: 0.9336\n",
      "Epoch: 159/160 Train Loss: 0.0037 Accuracy: 0.9990 Time: 9.94608  | Val Loss: 0.3984 Accuracy: 0.9348\n",
      "Epoch: 160/160 Train Loss: 0.0034 Accuracy: 0.9991 Time: 9.98035  | Val Loss: 0.3957 Accuracy: 0.9360\n",
      "#Parameter: 21282122 Accuracy: 0.936\n"
     ]
    }
   ],
   "source": [
    "from hdd.models.cnn.resnet import resnet34_config\n",
    "\n",
    "net, resnet34_stats = train_net(\n",
    "    resnet34_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout=0.5,\n",
    "    lr=0.01,\n",
    "    weight_decay=1e-1,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "faac6618",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/160 Train Loss: 1.8959 Accuracy: 0.2963 Time: 20.14928  | Val Loss: 1.7417 Accuracy: 0.3973\n",
      "Epoch: 2/160 Train Loss: 1.3965 Accuracy: 0.4864 Time: 20.12726  | Val Loss: 1.4714 Accuracy: 0.4988\n",
      "Epoch: 3/160 Train Loss: 1.1553 Accuracy: 0.5835 Time: 20.13189  | Val Loss: 1.5966 Accuracy: 0.5040\n",
      "Epoch: 4/160 Train Loss: 1.0375 Accuracy: 0.6342 Time: 20.15878  | Val Loss: 1.5955 Accuracy: 0.4909\n",
      "Epoch: 5/160 Train Loss: 0.9588 Accuracy: 0.6608 Time: 20.28256  | Val Loss: 1.2455 Accuracy: 0.5747\n",
      "Epoch: 6/160 Train Loss: 0.9195 Accuracy: 0.6757 Time: 20.17661  | Val Loss: 1.1823 Accuracy: 0.5929\n",
      "Epoch: 7/160 Train Loss: 0.8793 Accuracy: 0.6937 Time: 20.26094  | Val Loss: 1.2263 Accuracy: 0.5791\n",
      "Epoch: 8/160 Train Loss: 0.8520 Accuracy: 0.7051 Time: 20.17022  | Val Loss: 1.6649 Accuracy: 0.5131\n",
      "Epoch: 9/160 Train Loss: 0.8250 Accuracy: 0.7138 Time: 20.27788  | Val Loss: 1.4251 Accuracy: 0.5784\n",
      "Epoch: 10/160 Train Loss: 0.8168 Accuracy: 0.7181 Time: 20.19590  | Val Loss: 1.2131 Accuracy: 0.6133\n",
      "Epoch: 11/160 Train Loss: 0.7925 Accuracy: 0.7271 Time: 20.20243  | Val Loss: 1.1267 Accuracy: 0.6334\n",
      "Epoch: 12/160 Train Loss: 0.7793 Accuracy: 0.7306 Time: 20.24597  | Val Loss: 1.1345 Accuracy: 0.6349\n",
      "Epoch: 13/160 Train Loss: 0.7617 Accuracy: 0.7380 Time: 20.27519  | Val Loss: 0.8427 Accuracy: 0.7172\n",
      "Epoch: 14/160 Train Loss: 0.7538 Accuracy: 0.7432 Time: 20.23915  | Val Loss: 2.1398 Accuracy: 0.4595\n",
      "Epoch: 15/160 Train Loss: 0.7506 Accuracy: 0.7429 Time: 20.30150  | Val Loss: 1.0625 Accuracy: 0.6602\n",
      "Epoch: 16/160 Train Loss: 0.7422 Accuracy: 0.7454 Time: 20.22490  | Val Loss: 2.2608 Accuracy: 0.4518\n",
      "Epoch: 17/160 Train Loss: 0.7346 Accuracy: 0.7501 Time: 20.21959  | Val Loss: 1.0070 Accuracy: 0.6597\n",
      "Epoch: 18/160 Train Loss: 0.7284 Accuracy: 0.7512 Time: 20.27041  | Val Loss: 1.2649 Accuracy: 0.6002\n",
      "Epoch: 19/160 Train Loss: 0.7297 Accuracy: 0.7507 Time: 20.14847  | Val Loss: 1.2665 Accuracy: 0.6241\n",
      "Epoch: 20/160 Train Loss: 0.7224 Accuracy: 0.7545 Time: 20.15222  | Val Loss: 1.0808 Accuracy: 0.6445\n",
      "Epoch: 21/160 Train Loss: 0.7194 Accuracy: 0.7543 Time: 20.12540  | Val Loss: 1.5289 Accuracy: 0.5900\n",
      "Epoch: 22/160 Train Loss: 0.7171 Accuracy: 0.7564 Time: 20.12674  | Val Loss: 0.8964 Accuracy: 0.7047\n",
      "Epoch: 23/160 Train Loss: 0.7206 Accuracy: 0.7563 Time: 20.23747  | Val Loss: 1.0712 Accuracy: 0.6479\n",
      "Epoch: 24/160 Train Loss: 0.7101 Accuracy: 0.7588 Time: 20.28512  | Val Loss: 1.3194 Accuracy: 0.6132\n",
      "Epoch: 25/160 Train Loss: 0.7132 Accuracy: 0.7556 Time: 20.77975  | Val Loss: 0.9034 Accuracy: 0.7030\n",
      "Epoch: 26/160 Train Loss: 0.7106 Accuracy: 0.7565 Time: 21.76027  | Val Loss: 1.0402 Accuracy: 0.6597\n",
      "Epoch: 27/160 Train Loss: 0.7104 Accuracy: 0.7562 Time: 21.51346  | Val Loss: 1.1397 Accuracy: 0.6639\n",
      "Epoch: 28/160 Train Loss: 0.7143 Accuracy: 0.7569 Time: 21.71582  | Val Loss: 0.9086 Accuracy: 0.7000\n",
      "Epoch: 29/160 Train Loss: 0.7186 Accuracy: 0.7540 Time: 21.67411  | Val Loss: 0.9641 Accuracy: 0.6849\n",
      "Epoch: 30/160 Train Loss: 0.7062 Accuracy: 0.7580 Time: 21.63520  | Val Loss: 0.9909 Accuracy: 0.6813\n",
      "Epoch: 31/160 Train Loss: 0.7099 Accuracy: 0.7581 Time: 21.64884  | Val Loss: 1.6734 Accuracy: 0.5738\n",
      "Epoch: 32/160 Train Loss: 0.7183 Accuracy: 0.7540 Time: 21.44491  | Val Loss: 1.1666 Accuracy: 0.6507\n",
      "Epoch: 33/160 Train Loss: 0.7146 Accuracy: 0.7580 Time: 21.53788  | Val Loss: 1.0734 Accuracy: 0.6559\n",
      "Epoch: 34/160 Train Loss: 0.7112 Accuracy: 0.7550 Time: 21.37156  | Val Loss: 0.9102 Accuracy: 0.6955\n",
      "Epoch: 35/160 Train Loss: 0.7130 Accuracy: 0.7583 Time: 21.53603  | Val Loss: 0.7970 Accuracy: 0.7339\n",
      "Epoch: 36/160 Train Loss: 0.7188 Accuracy: 0.7558 Time: 21.69551  | Val Loss: 0.9030 Accuracy: 0.7082\n",
      "Epoch: 37/160 Train Loss: 0.7134 Accuracy: 0.7561 Time: 21.52204  | Val Loss: 1.1150 Accuracy: 0.6601\n",
      "Epoch: 38/160 Train Loss: 0.7139 Accuracy: 0.7576 Time: 21.58281  | Val Loss: 1.0393 Accuracy: 0.6560\n",
      "Epoch: 39/160 Train Loss: 0.7207 Accuracy: 0.7529 Time: 21.51946  | Val Loss: 1.2280 Accuracy: 0.6268\n",
      "Epoch: 40/160 Train Loss: 0.7117 Accuracy: 0.7574 Time: 21.53794  | Val Loss: 1.1674 Accuracy: 0.6532\n",
      "Epoch: 41/160 Train Loss: 0.4867 Accuracy: 0.8330 Time: 21.51364  | Val Loss: 0.4157 Accuracy: 0.8577\n",
      "Epoch: 42/160 Train Loss: 0.4144 Accuracy: 0.8581 Time: 20.13620  | Val Loss: 0.4057 Accuracy: 0.8578\n",
      "Epoch: 43/160 Train Loss: 0.3884 Accuracy: 0.8659 Time: 20.26223  | Val Loss: 0.3954 Accuracy: 0.8624\n",
      "Epoch: 44/160 Train Loss: 0.3649 Accuracy: 0.8746 Time: 20.20610  | Val Loss: 0.4060 Accuracy: 0.8628\n",
      "Epoch: 45/160 Train Loss: 0.3469 Accuracy: 0.8807 Time: 20.24200  | Val Loss: 0.3789 Accuracy: 0.8708\n",
      "Epoch: 46/160 Train Loss: 0.3364 Accuracy: 0.8848 Time: 20.29262  | Val Loss: 0.3731 Accuracy: 0.8737\n",
      "Epoch: 47/160 Train Loss: 0.3256 Accuracy: 0.8865 Time: 20.18395  | Val Loss: 0.3811 Accuracy: 0.8723\n",
      "Epoch: 48/160 Train Loss: 0.3165 Accuracy: 0.8910 Time: 20.21691  | Val Loss: 0.3642 Accuracy: 0.8783\n",
      "Epoch: 49/160 Train Loss: 0.3084 Accuracy: 0.8923 Time: 20.14087  | Val Loss: 0.3711 Accuracy: 0.8757\n",
      "Epoch: 50/160 Train Loss: 0.2982 Accuracy: 0.8963 Time: 20.13742  | Val Loss: 0.3655 Accuracy: 0.8785\n",
      "Epoch: 51/160 Train Loss: 0.2938 Accuracy: 0.8990 Time: 20.17161  | Val Loss: 0.3496 Accuracy: 0.8804\n",
      "Epoch: 52/160 Train Loss: 0.2819 Accuracy: 0.9008 Time: 20.22088  | Val Loss: 0.3514 Accuracy: 0.8803\n",
      "Epoch: 53/160 Train Loss: 0.2821 Accuracy: 0.9021 Time: 20.13707  | Val Loss: 0.3522 Accuracy: 0.8860\n",
      "Epoch: 54/160 Train Loss: 0.2774 Accuracy: 0.9030 Time: 20.15016  | Val Loss: 0.3746 Accuracy: 0.8783\n",
      "Epoch: 55/160 Train Loss: 0.2701 Accuracy: 0.9060 Time: 20.15181  | Val Loss: 0.3570 Accuracy: 0.8803\n",
      "Epoch: 56/160 Train Loss: 0.2682 Accuracy: 0.9062 Time: 20.14352  | Val Loss: 0.3413 Accuracy: 0.8874\n",
      "Epoch: 57/160 Train Loss: 0.2589 Accuracy: 0.9111 Time: 20.21238  | Val Loss: 0.3902 Accuracy: 0.8742\n",
      "Epoch: 58/160 Train Loss: 0.2622 Accuracy: 0.9083 Time: 20.20175  | Val Loss: 0.3951 Accuracy: 0.8723\n",
      "Epoch: 59/160 Train Loss: 0.2584 Accuracy: 0.9095 Time: 20.22242  | Val Loss: 0.3894 Accuracy: 0.8706\n",
      "Epoch: 60/160 Train Loss: 0.2524 Accuracy: 0.9130 Time: 20.31890  | Val Loss: 0.3940 Accuracy: 0.8762\n",
      "Epoch: 61/160 Train Loss: 0.2543 Accuracy: 0.9118 Time: 20.24640  | Val Loss: 0.4051 Accuracy: 0.8732\n",
      "Epoch: 62/160 Train Loss: 0.2499 Accuracy: 0.9128 Time: 20.19696  | Val Loss: 0.3907 Accuracy: 0.8796\n",
      "Epoch: 63/160 Train Loss: 0.2486 Accuracy: 0.9143 Time: 20.11779  | Val Loss: 0.3724 Accuracy: 0.8802\n",
      "Epoch: 64/160 Train Loss: 0.2486 Accuracy: 0.9132 Time: 20.23175  | Val Loss: 0.3970 Accuracy: 0.8717\n",
      "Epoch: 65/160 Train Loss: 0.2434 Accuracy: 0.9149 Time: 20.11864  | Val Loss: 0.3689 Accuracy: 0.8808\n",
      "Epoch: 66/160 Train Loss: 0.2462 Accuracy: 0.9142 Time: 20.16882  | Val Loss: 0.3556 Accuracy: 0.8859\n",
      "Epoch: 67/160 Train Loss: 0.2450 Accuracy: 0.9145 Time: 20.14121  | Val Loss: 0.3846 Accuracy: 0.8756\n",
      "Epoch: 68/160 Train Loss: 0.2468 Accuracy: 0.9145 Time: 20.18194  | Val Loss: 0.4258 Accuracy: 0.8676\n",
      "Epoch: 69/160 Train Loss: 0.2426 Accuracy: 0.9160 Time: 20.19384  | Val Loss: 0.3837 Accuracy: 0.8727\n",
      "Epoch: 70/160 Train Loss: 0.2423 Accuracy: 0.9159 Time: 20.27755  | Val Loss: 0.4057 Accuracy: 0.8760\n",
      "Epoch: 71/160 Train Loss: 0.2432 Accuracy: 0.9143 Time: 20.22544  | Val Loss: 0.4330 Accuracy: 0.8669\n",
      "Epoch: 72/160 Train Loss: 0.2460 Accuracy: 0.9139 Time: 20.24530  | Val Loss: 0.3960 Accuracy: 0.8713\n",
      "Epoch: 73/160 Train Loss: 0.2400 Accuracy: 0.9172 Time: 20.12911  | Val Loss: 0.3999 Accuracy: 0.8759\n",
      "Epoch: 74/160 Train Loss: 0.2401 Accuracy: 0.9153 Time: 20.20197  | Val Loss: 0.3834 Accuracy: 0.8767\n",
      "Epoch: 75/160 Train Loss: 0.2432 Accuracy: 0.9138 Time: 20.12717  | Val Loss: 0.4102 Accuracy: 0.8694\n",
      "Epoch: 76/160 Train Loss: 0.2435 Accuracy: 0.9147 Time: 20.13163  | Val Loss: 0.4134 Accuracy: 0.8747\n",
      "Epoch: 77/160 Train Loss: 0.2463 Accuracy: 0.9136 Time: 20.17405  | Val Loss: 0.4086 Accuracy: 0.8666\n",
      "Epoch: 78/160 Train Loss: 0.2396 Accuracy: 0.9162 Time: 20.13415  | Val Loss: 0.3830 Accuracy: 0.8784\n",
      "Epoch: 79/160 Train Loss: 0.2422 Accuracy: 0.9157 Time: 20.20128  | Val Loss: 0.4394 Accuracy: 0.8648\n",
      "Epoch: 80/160 Train Loss: 0.2390 Accuracy: 0.9163 Time: 20.21400  | Val Loss: 0.4586 Accuracy: 0.8607\n",
      "Epoch: 81/160 Train Loss: 0.1672 Accuracy: 0.9430 Time: 20.17502  | Val Loss: 0.2860 Accuracy: 0.9105\n",
      "Epoch: 82/160 Train Loss: 0.1407 Accuracy: 0.9509 Time: 20.14977  | Val Loss: 0.2797 Accuracy: 0.9114\n",
      "Epoch: 83/160 Train Loss: 0.1299 Accuracy: 0.9546 Time: 20.13778  | Val Loss: 0.2809 Accuracy: 0.9118\n",
      "Epoch: 84/160 Train Loss: 0.1201 Accuracy: 0.9589 Time: 20.11562  | Val Loss: 0.2816 Accuracy: 0.9126\n",
      "Epoch: 85/160 Train Loss: 0.1155 Accuracy: 0.9607 Time: 20.10934  | Val Loss: 0.2888 Accuracy: 0.9119\n",
      "Epoch: 86/160 Train Loss: 0.1122 Accuracy: 0.9613 Time: 20.12718  | Val Loss: 0.2851 Accuracy: 0.9144\n",
      "Epoch: 87/160 Train Loss: 0.1078 Accuracy: 0.9625 Time: 20.31310  | Val Loss: 0.2916 Accuracy: 0.9103\n",
      "Epoch: 88/160 Train Loss: 0.1023 Accuracy: 0.9644 Time: 20.25021  | Val Loss: 0.2911 Accuracy: 0.9130\n",
      "Epoch: 89/160 Train Loss: 0.1002 Accuracy: 0.9650 Time: 20.11231  | Val Loss: 0.2986 Accuracy: 0.9111\n",
      "Epoch: 90/160 Train Loss: 0.0956 Accuracy: 0.9669 Time: 20.10479  | Val Loss: 0.2982 Accuracy: 0.9128\n",
      "Epoch: 91/160 Train Loss: 0.0943 Accuracy: 0.9680 Time: 20.19812  | Val Loss: 0.3045 Accuracy: 0.9127\n",
      "Epoch: 92/160 Train Loss: 0.0921 Accuracy: 0.9683 Time: 20.21635  | Val Loss: 0.3110 Accuracy: 0.9121\n",
      "Epoch: 93/160 Train Loss: 0.0902 Accuracy: 0.9687 Time: 20.25444  | Val Loss: 0.3084 Accuracy: 0.9125\n",
      "Epoch: 94/160 Train Loss: 0.0884 Accuracy: 0.9695 Time: 20.22533  | Val Loss: 0.3122 Accuracy: 0.9129\n",
      "Epoch: 95/160 Train Loss: 0.0831 Accuracy: 0.9714 Time: 20.16003  | Val Loss: 0.3145 Accuracy: 0.9122\n",
      "Epoch: 96/160 Train Loss: 0.0802 Accuracy: 0.9717 Time: 20.22972  | Val Loss: 0.3208 Accuracy: 0.9101\n",
      "Epoch: 97/160 Train Loss: 0.0785 Accuracy: 0.9728 Time: 20.24570  | Val Loss: 0.3298 Accuracy: 0.9099\n",
      "Epoch: 98/160 Train Loss: 0.0787 Accuracy: 0.9733 Time: 20.24003  | Val Loss: 0.3275 Accuracy: 0.9108\n",
      "Epoch: 99/160 Train Loss: 0.0747 Accuracy: 0.9740 Time: 20.26720  | Val Loss: 0.3279 Accuracy: 0.9125\n",
      "Epoch: 100/160 Train Loss: 0.0750 Accuracy: 0.9730 Time: 20.28488  | Val Loss: 0.3306 Accuracy: 0.9109\n",
      "Epoch: 101/160 Train Loss: 0.0722 Accuracy: 0.9746 Time: 20.24876  | Val Loss: 0.3305 Accuracy: 0.9128\n",
      "Epoch: 102/160 Train Loss: 0.0724 Accuracy: 0.9742 Time: 20.13526  | Val Loss: 0.3284 Accuracy: 0.9114\n",
      "Epoch: 103/160 Train Loss: 0.0680 Accuracy: 0.9756 Time: 20.14294  | Val Loss: 0.3525 Accuracy: 0.9090\n",
      "Epoch: 104/160 Train Loss: 0.0697 Accuracy: 0.9757 Time: 20.12823  | Val Loss: 0.3329 Accuracy: 0.9131\n",
      "Epoch: 105/160 Train Loss: 0.0683 Accuracy: 0.9764 Time: 20.20778  | Val Loss: 0.3438 Accuracy: 0.9103\n",
      "Epoch: 106/160 Train Loss: 0.0664 Accuracy: 0.9770 Time: 20.23315  | Val Loss: 0.3530 Accuracy: 0.9097\n",
      "Epoch: 107/160 Train Loss: 0.0665 Accuracy: 0.9770 Time: 20.27033  | Val Loss: 0.3478 Accuracy: 0.9099\n",
      "Epoch: 108/160 Train Loss: 0.0623 Accuracy: 0.9785 Time: 20.27833  | Val Loss: 0.3454 Accuracy: 0.9118\n",
      "Epoch: 109/160 Train Loss: 0.0610 Accuracy: 0.9796 Time: 20.14024  | Val Loss: 0.3552 Accuracy: 0.9095\n",
      "Epoch: 110/160 Train Loss: 0.0614 Accuracy: 0.9787 Time: 20.13016  | Val Loss: 0.3444 Accuracy: 0.9139\n",
      "Epoch: 111/160 Train Loss: 0.0589 Accuracy: 0.9791 Time: 20.18793  | Val Loss: 0.3548 Accuracy: 0.9110\n",
      "Epoch: 112/160 Train Loss: 0.0615 Accuracy: 0.9779 Time: 20.20839  | Val Loss: 0.3554 Accuracy: 0.9082\n",
      "Epoch: 113/160 Train Loss: 0.0562 Accuracy: 0.9806 Time: 20.14617  | Val Loss: 0.3544 Accuracy: 0.9104\n",
      "Epoch: 114/160 Train Loss: 0.0599 Accuracy: 0.9793 Time: 20.12226  | Val Loss: 0.3602 Accuracy: 0.9119\n",
      "Epoch: 115/160 Train Loss: 0.0546 Accuracy: 0.9811 Time: 20.16225  | Val Loss: 0.3557 Accuracy: 0.9118\n",
      "Epoch: 116/160 Train Loss: 0.0556 Accuracy: 0.9807 Time: 20.21559  | Val Loss: 0.3692 Accuracy: 0.9094\n",
      "Epoch: 117/160 Train Loss: 0.0564 Accuracy: 0.9800 Time: 20.20256  | Val Loss: 0.3692 Accuracy: 0.9103\n",
      "Epoch: 118/160 Train Loss: 0.0543 Accuracy: 0.9808 Time: 20.25643  | Val Loss: 0.3772 Accuracy: 0.9106\n",
      "Epoch: 119/160 Train Loss: 0.0541 Accuracy: 0.9806 Time: 20.22823  | Val Loss: 0.3829 Accuracy: 0.9094\n",
      "Epoch: 120/160 Train Loss: 0.0515 Accuracy: 0.9822 Time: 20.20689  | Val Loss: 0.3710 Accuracy: 0.9107\n",
      "Epoch: 121/160 Train Loss: 0.0427 Accuracy: 0.9856 Time: 20.19707  | Val Loss: 0.3690 Accuracy: 0.9101\n",
      "Epoch: 122/160 Train Loss: 0.0449 Accuracy: 0.9842 Time: 20.13085  | Val Loss: 0.3667 Accuracy: 0.9108\n",
      "Epoch: 123/160 Train Loss: 0.0420 Accuracy: 0.9857 Time: 20.12886  | Val Loss: 0.3666 Accuracy: 0.9119\n",
      "Epoch: 124/160 Train Loss: 0.0412 Accuracy: 0.9859 Time: 20.12922  | Val Loss: 0.3680 Accuracy: 0.9112\n",
      "Epoch: 125/160 Train Loss: 0.0415 Accuracy: 0.9859 Time: 20.21967  | Val Loss: 0.3681 Accuracy: 0.9113\n",
      "Epoch: 126/160 Train Loss: 0.0391 Accuracy: 0.9873 Time: 20.34368  | Val Loss: 0.3682 Accuracy: 0.9093\n",
      "Epoch: 127/160 Train Loss: 0.0399 Accuracy: 0.9866 Time: 20.25069  | Val Loss: 0.3688 Accuracy: 0.9097\n",
      "Epoch: 128/160 Train Loss: 0.0404 Accuracy: 0.9857 Time: 20.22805  | Val Loss: 0.3702 Accuracy: 0.9118\n",
      "Epoch: 129/160 Train Loss: 0.0392 Accuracy: 0.9869 Time: 20.16552  | Val Loss: 0.3687 Accuracy: 0.9116\n",
      "Epoch: 130/160 Train Loss: 0.0387 Accuracy: 0.9866 Time: 20.13560  | Val Loss: 0.3724 Accuracy: 0.9115\n",
      "Epoch: 131/160 Train Loss: 0.0376 Accuracy: 0.9873 Time: 20.11858  | Val Loss: 0.3730 Accuracy: 0.9113\n",
      "Epoch: 132/160 Train Loss: 0.0378 Accuracy: 0.9877 Time: 20.23677  | Val Loss: 0.3710 Accuracy: 0.9111\n",
      "Epoch: 133/160 Train Loss: 0.0370 Accuracy: 0.9882 Time: 20.26933  | Val Loss: 0.3717 Accuracy: 0.9120\n",
      "Epoch: 134/160 Train Loss: 0.0370 Accuracy: 0.9881 Time: 20.26208  | Val Loss: 0.3688 Accuracy: 0.9127\n",
      "Epoch: 135/160 Train Loss: 0.0363 Accuracy: 0.9880 Time: 20.24546  | Val Loss: 0.3724 Accuracy: 0.9119\n",
      "Epoch: 136/160 Train Loss: 0.0387 Accuracy: 0.9867 Time: 20.30649  | Val Loss: 0.3700 Accuracy: 0.9113\n",
      "Epoch: 137/160 Train Loss: 0.0373 Accuracy: 0.9877 Time: 20.11503  | Val Loss: 0.3685 Accuracy: 0.9110\n",
      "Epoch: 138/160 Train Loss: 0.0370 Accuracy: 0.9875 Time: 20.19118  | Val Loss: 0.3742 Accuracy: 0.9119\n",
      "Epoch: 139/160 Train Loss: 0.0385 Accuracy: 0.9867 Time: 20.23783  | Val Loss: 0.3721 Accuracy: 0.9108\n",
      "Epoch: 140/160 Train Loss: 0.0364 Accuracy: 0.9879 Time: 20.11771  | Val Loss: 0.3725 Accuracy: 0.9115\n",
      "Epoch: 141/160 Train Loss: 0.0349 Accuracy: 0.9885 Time: 20.12202  | Val Loss: 0.3731 Accuracy: 0.9116\n",
      "Epoch: 142/160 Train Loss: 0.0387 Accuracy: 0.9871 Time: 20.11002  | Val Loss: 0.3794 Accuracy: 0.9105\n",
      "Epoch: 143/160 Train Loss: 0.0369 Accuracy: 0.9871 Time: 20.20354  | Val Loss: 0.3779 Accuracy: 0.9115\n",
      "Epoch: 144/160 Train Loss: 0.0369 Accuracy: 0.9880 Time: 20.26910  | Val Loss: 0.3734 Accuracy: 0.9107\n",
      "Epoch: 145/160 Train Loss: 0.0348 Accuracy: 0.9883 Time: 20.19643  | Val Loss: 0.3764 Accuracy: 0.9106\n",
      "Epoch: 146/160 Train Loss: 0.0367 Accuracy: 0.9878 Time: 20.13863  | Val Loss: 0.3771 Accuracy: 0.9124\n",
      "Epoch: 147/160 Train Loss: 0.0341 Accuracy: 0.9884 Time: 20.24351  | Val Loss: 0.3804 Accuracy: 0.9106\n",
      "Epoch: 148/160 Train Loss: 0.0358 Accuracy: 0.9875 Time: 20.18494  | Val Loss: 0.3759 Accuracy: 0.9132\n",
      "Epoch: 149/160 Train Loss: 0.0350 Accuracy: 0.9885 Time: 20.14659  | Val Loss: 0.3767 Accuracy: 0.9108\n",
      "Epoch: 150/160 Train Loss: 0.0343 Accuracy: 0.9891 Time: 20.26998  | Val Loss: 0.3776 Accuracy: 0.9110\n",
      "Epoch: 151/160 Train Loss: 0.0350 Accuracy: 0.9884 Time: 20.14830  | Val Loss: 0.3760 Accuracy: 0.9117\n",
      "Epoch: 152/160 Train Loss: 0.0364 Accuracy: 0.9872 Time: 20.23361  | Val Loss: 0.3799 Accuracy: 0.9110\n",
      "Epoch: 153/160 Train Loss: 0.0339 Accuracy: 0.9887 Time: 20.18876  | Val Loss: 0.3826 Accuracy: 0.9110\n",
      "Epoch: 154/160 Train Loss: 0.0331 Accuracy: 0.9886 Time: 20.21415  | Val Loss: 0.3805 Accuracy: 0.9114\n",
      "Epoch: 155/160 Train Loss: 0.0342 Accuracy: 0.9885 Time: 20.12194  | Val Loss: 0.3802 Accuracy: 0.9115\n",
      "Epoch: 156/160 Train Loss: 0.0346 Accuracy: 0.9888 Time: 20.15072  | Val Loss: 0.3778 Accuracy: 0.9112\n",
      "Epoch: 157/160 Train Loss: 0.0329 Accuracy: 0.9891 Time: 20.12286  | Val Loss: 0.3824 Accuracy: 0.9109\n",
      "Epoch: 158/160 Train Loss: 0.0330 Accuracy: 0.9888 Time: 20.22449  | Val Loss: 0.3818 Accuracy: 0.9115\n",
      "Epoch: 159/160 Train Loss: 0.0339 Accuracy: 0.9884 Time: 20.20913  | Val Loss: 0.3868 Accuracy: 0.9106\n",
      "Epoch: 160/160 Train Loss: 0.0360 Accuracy: 0.9877 Time: 20.22402  | Val Loss: 0.3864 Accuracy: 0.9097\n",
      "#Parameter: 23520842 Accuracy: 0.9097\n"
     ]
    }
   ],
   "source": [
    "from hdd.models.cnn.resnet import resnet50_config\n",
    "\n",
    "net, resnet50_stats = train_net(\n",
    "    resnet50_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout=0.5,\n",
    "    lr=0.01,\n",
    "    weight_decay=1e-1,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "19a5e7fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/160 Train Loss: 1.9272 Accuracy: 0.2755 Time: 31.19245  | Val Loss: 1.4986 Accuracy: 0.4317\n",
      "Epoch: 2/160 Train Loss: 1.4003 Accuracy: 0.4858 Time: 31.24354  | Val Loss: 1.3055 Accuracy: 0.5078\n",
      "Epoch: 3/160 Train Loss: 1.1707 Accuracy: 0.5798 Time: 31.26345  | Val Loss: 1.1670 Accuracy: 0.5901\n",
      "Epoch: 4/160 Train Loss: 1.0435 Accuracy: 0.6295 Time: 31.25911  | Val Loss: 0.9785 Accuracy: 0.6599\n",
      "Epoch: 5/160 Train Loss: 0.9744 Accuracy: 0.6535 Time: 31.26375  | Val Loss: 1.2549 Accuracy: 0.5797\n",
      "Epoch: 6/160 Train Loss: 0.9295 Accuracy: 0.6736 Time: 31.24137  | Val Loss: 1.1797 Accuracy: 0.5941\n",
      "Epoch: 7/160 Train Loss: 0.8956 Accuracy: 0.6846 Time: 31.22354  | Val Loss: 1.3562 Accuracy: 0.5508\n",
      "Epoch: 8/160 Train Loss: 0.8646 Accuracy: 0.7005 Time: 31.25130  | Val Loss: 1.0283 Accuracy: 0.6473\n",
      "Epoch: 9/160 Train Loss: 0.8432 Accuracy: 0.7072 Time: 31.23822  | Val Loss: 1.3356 Accuracy: 0.5657\n",
      "Epoch: 10/160 Train Loss: 0.8173 Accuracy: 0.7208 Time: 31.23483  | Val Loss: 1.5366 Accuracy: 0.5693\n",
      "Epoch: 11/160 Train Loss: 0.8026 Accuracy: 0.7218 Time: 31.21506  | Val Loss: 0.9181 Accuracy: 0.6976\n",
      "Epoch: 12/160 Train Loss: 0.7821 Accuracy: 0.7302 Time: 31.20377  | Val Loss: 1.7157 Accuracy: 0.5327\n",
      "Epoch: 13/160 Train Loss: 0.7657 Accuracy: 0.7371 Time: 31.21865  | Val Loss: 1.0818 Accuracy: 0.6466\n",
      "Epoch: 14/160 Train Loss: 0.7609 Accuracy: 0.7376 Time: 31.18977  | Val Loss: 1.9459 Accuracy: 0.5497\n",
      "Epoch: 15/160 Train Loss: 0.7413 Accuracy: 0.7444 Time: 31.20415  | Val Loss: 0.8994 Accuracy: 0.7054\n",
      "Epoch: 16/160 Train Loss: 0.7388 Accuracy: 0.7464 Time: 31.18622  | Val Loss: 1.1426 Accuracy: 0.6326\n",
      "Epoch: 17/160 Train Loss: 0.7292 Accuracy: 0.7503 Time: 31.19732  | Val Loss: 1.5329 Accuracy: 0.5817\n",
      "Epoch: 18/160 Train Loss: 0.7231 Accuracy: 0.7545 Time: 31.20746  | Val Loss: 11.1520 Accuracy: 0.4020\n",
      "Epoch: 19/160 Train Loss: 0.7243 Accuracy: 0.7535 Time: 31.19635  | Val Loss: 1.2056 Accuracy: 0.6087\n",
      "Epoch: 20/160 Train Loss: 0.7245 Accuracy: 0.7546 Time: 31.22863  | Val Loss: 1.7127 Accuracy: 0.5455\n",
      "Epoch: 21/160 Train Loss: 0.7189 Accuracy: 0.7560 Time: 31.19865  | Val Loss: 1.1399 Accuracy: 0.6508\n",
      "Epoch: 22/160 Train Loss: 0.7218 Accuracy: 0.7531 Time: 31.24872  | Val Loss: 1.6256 Accuracy: 0.5714\n",
      "Epoch: 23/160 Train Loss: 0.7194 Accuracy: 0.7546 Time: 31.20567  | Val Loss: 2.1672 Accuracy: 0.5059\n",
      "Epoch: 24/160 Train Loss: 0.7165 Accuracy: 0.7590 Time: 31.19796  | Val Loss: 1.2339 Accuracy: 0.6104\n",
      "Epoch: 25/160 Train Loss: 0.7134 Accuracy: 0.7587 Time: 31.19098  | Val Loss: 1.3226 Accuracy: 0.6078\n",
      "Epoch: 26/160 Train Loss: 0.7086 Accuracy: 0.7597 Time: 31.18738  | Val Loss: 1.0991 Accuracy: 0.6479\n",
      "Epoch: 27/160 Train Loss: 0.7177 Accuracy: 0.7539 Time: 31.17797  | Val Loss: 0.9094 Accuracy: 0.6951\n",
      "Epoch: 28/160 Train Loss: 0.7116 Accuracy: 0.7584 Time: 31.19156  | Val Loss: 1.6786 Accuracy: 0.5494\n",
      "Epoch: 29/160 Train Loss: 0.7178 Accuracy: 0.7548 Time: 31.15655  | Val Loss: 1.9325 Accuracy: 0.5332\n",
      "Epoch: 30/160 Train Loss: 0.7114 Accuracy: 0.7578 Time: 31.23697  | Val Loss: 1.2029 Accuracy: 0.6019\n",
      "Epoch: 31/160 Train Loss: 0.7148 Accuracy: 0.7566 Time: 31.14331  | Val Loss: 0.8497 Accuracy: 0.7083\n",
      "Epoch: 32/160 Train Loss: 0.7171 Accuracy: 0.7568 Time: 31.15253  | Val Loss: 1.5423 Accuracy: 0.5876\n",
      "Epoch: 33/160 Train Loss: 0.7065 Accuracy: 0.7610 Time: 31.14861  | Val Loss: 1.6251 Accuracy: 0.5494\n",
      "Epoch: 34/160 Train Loss: 0.7145 Accuracy: 0.7551 Time: 31.14355  | Val Loss: 1.1296 Accuracy: 0.6565\n",
      "Epoch: 35/160 Train Loss: 0.7166 Accuracy: 0.7555 Time: 31.12881  | Val Loss: 1.3171 Accuracy: 0.6244\n",
      "Epoch: 36/160 Train Loss: 0.7127 Accuracy: 0.7596 Time: 31.13195  | Val Loss: 0.8689 Accuracy: 0.7199\n",
      "Epoch: 37/160 Train Loss: 0.7172 Accuracy: 0.7559 Time: 31.12659  | Val Loss: 0.9980 Accuracy: 0.6644\n",
      "Epoch: 38/160 Train Loss: 0.7154 Accuracy: 0.7553 Time: 31.13136  | Val Loss: 1.4706 Accuracy: 0.6017\n",
      "Epoch: 39/160 Train Loss: 0.7133 Accuracy: 0.7567 Time: 31.16135  | Val Loss: 1.3013 Accuracy: 0.6043\n",
      "Epoch: 40/160 Train Loss: 0.7165 Accuracy: 0.7565 Time: 31.12041  | Val Loss: 0.9824 Accuracy: 0.6701\n",
      "Epoch: 41/160 Train Loss: 0.4831 Accuracy: 0.8372 Time: 31.15943  | Val Loss: 0.4261 Accuracy: 0.8568\n",
      "Epoch: 42/160 Train Loss: 0.4178 Accuracy: 0.8568 Time: 31.13255  | Val Loss: 0.4196 Accuracy: 0.8600\n",
      "Epoch: 43/160 Train Loss: 0.3939 Accuracy: 0.8649 Time: 31.13624  | Val Loss: 0.4036 Accuracy: 0.8619\n",
      "Epoch: 44/160 Train Loss: 0.3688 Accuracy: 0.8738 Time: 31.13739  | Val Loss: 0.4133 Accuracy: 0.8613\n",
      "Epoch: 45/160 Train Loss: 0.3541 Accuracy: 0.8782 Time: 31.13890  | Val Loss: 0.3788 Accuracy: 0.8729\n",
      "Epoch: 46/160 Train Loss: 0.3423 Accuracy: 0.8830 Time: 31.13062  | Val Loss: 0.3929 Accuracy: 0.8728\n",
      "Epoch: 47/160 Train Loss: 0.3336 Accuracy: 0.8838 Time: 31.14288  | Val Loss: 0.3798 Accuracy: 0.8738\n",
      "Epoch: 48/160 Train Loss: 0.3208 Accuracy: 0.8894 Time: 31.14886  | Val Loss: 0.4053 Accuracy: 0.8651\n",
      "Epoch: 49/160 Train Loss: 0.3146 Accuracy: 0.8915 Time: 31.16014  | Val Loss: 0.3784 Accuracy: 0.8759\n",
      "Epoch: 50/160 Train Loss: 0.3003 Accuracy: 0.8974 Time: 31.12578  | Val Loss: 0.4240 Accuracy: 0.8610\n",
      "Epoch: 51/160 Train Loss: 0.2974 Accuracy: 0.8972 Time: 31.14181  | Val Loss: 0.3527 Accuracy: 0.8841\n",
      "Epoch: 52/160 Train Loss: 0.2950 Accuracy: 0.8991 Time: 31.13442  | Val Loss: 0.4316 Accuracy: 0.8606\n",
      "Epoch: 53/160 Train Loss: 0.2854 Accuracy: 0.9005 Time: 31.14008  | Val Loss: 0.3544 Accuracy: 0.8827\n",
      "Epoch: 54/160 Train Loss: 0.2812 Accuracy: 0.9031 Time: 31.14316  | Val Loss: 0.3839 Accuracy: 0.8760\n",
      "Epoch: 55/160 Train Loss: 0.2746 Accuracy: 0.9038 Time: 31.13741  | Val Loss: 0.3853 Accuracy: 0.8749\n",
      "Epoch: 56/160 Train Loss: 0.2742 Accuracy: 0.9059 Time: 31.22127  | Val Loss: 0.3951 Accuracy: 0.8730\n",
      "Epoch: 57/160 Train Loss: 0.2667 Accuracy: 0.9077 Time: 31.16150  | Val Loss: 0.3892 Accuracy: 0.8717\n",
      "Epoch: 58/160 Train Loss: 0.2694 Accuracy: 0.9059 Time: 31.14670  | Val Loss: 0.4294 Accuracy: 0.8647\n",
      "Epoch: 59/160 Train Loss: 0.2620 Accuracy: 0.9094 Time: 31.13006  | Val Loss: 0.4179 Accuracy: 0.8688\n",
      "Epoch: 60/160 Train Loss: 0.2637 Accuracy: 0.9076 Time: 31.15520  | Val Loss: 0.4297 Accuracy: 0.8680\n",
      "Epoch: 61/160 Train Loss: 0.2558 Accuracy: 0.9113 Time: 31.13020  | Val Loss: 0.6070 Accuracy: 0.8161\n",
      "Epoch: 62/160 Train Loss: 0.2596 Accuracy: 0.9084 Time: 31.13189  | Val Loss: 0.3814 Accuracy: 0.8749\n",
      "Epoch: 63/160 Train Loss: 0.2587 Accuracy: 0.9083 Time: 31.14298  | Val Loss: 0.4916 Accuracy: 0.8523\n",
      "Epoch: 64/160 Train Loss: 0.2547 Accuracy: 0.9126 Time: 31.11640  | Val Loss: 0.4280 Accuracy: 0.8655\n",
      "Epoch: 65/160 Train Loss: 0.2526 Accuracy: 0.9124 Time: 31.13867  | Val Loss: 0.4734 Accuracy: 0.8506\n",
      "Epoch: 66/160 Train Loss: 0.2542 Accuracy: 0.9123 Time: 31.15423  | Val Loss: 0.4050 Accuracy: 0.8687\n",
      "Epoch: 67/160 Train Loss: 0.2499 Accuracy: 0.9126 Time: 31.12486  | Val Loss: 0.4321 Accuracy: 0.8624\n",
      "Epoch: 68/160 Train Loss: 0.2511 Accuracy: 0.9139 Time: 31.13426  | Val Loss: 0.3611 Accuracy: 0.8820\n",
      "Epoch: 69/160 Train Loss: 0.2513 Accuracy: 0.9111 Time: 31.12559  | Val Loss: 0.4407 Accuracy: 0.8586\n",
      "Epoch: 70/160 Train Loss: 0.2528 Accuracy: 0.9122 Time: 31.13519  | Val Loss: 0.4716 Accuracy: 0.8584\n",
      "Epoch: 71/160 Train Loss: 0.2481 Accuracy: 0.9134 Time: 31.13857  | Val Loss: 0.3710 Accuracy: 0.8801\n",
      "Epoch: 72/160 Train Loss: 0.2504 Accuracy: 0.9118 Time: 31.12271  | Val Loss: 0.4273 Accuracy: 0.8675\n",
      "Epoch: 73/160 Train Loss: 0.2524 Accuracy: 0.9114 Time: 31.12642  | Val Loss: 0.3855 Accuracy: 0.8689\n",
      "Epoch: 74/160 Train Loss: 0.2468 Accuracy: 0.9139 Time: 31.12971  | Val Loss: 0.3866 Accuracy: 0.8789\n",
      "Epoch: 75/160 Train Loss: 0.2469 Accuracy: 0.9152 Time: 31.13089  | Val Loss: 0.4316 Accuracy: 0.8617\n",
      "Epoch: 76/160 Train Loss: 0.2475 Accuracy: 0.9139 Time: 31.13450  | Val Loss: 0.4613 Accuracy: 0.8562\n",
      "Epoch: 77/160 Train Loss: 0.2526 Accuracy: 0.9109 Time: 31.13304  | Val Loss: 0.4181 Accuracy: 0.8653\n",
      "Epoch: 78/160 Train Loss: 0.2498 Accuracy: 0.9138 Time: 31.12897  | Val Loss: 0.4064 Accuracy: 0.8686\n",
      "Epoch: 79/160 Train Loss: 0.2562 Accuracy: 0.9105 Time: 31.13956  | Val Loss: 0.4408 Accuracy: 0.8607\n",
      "Epoch: 80/160 Train Loss: 0.2455 Accuracy: 0.9143 Time: 31.13290  | Val Loss: 0.4371 Accuracy: 0.8597\n",
      "Epoch: 81/160 Train Loss: 0.1737 Accuracy: 0.9410 Time: 31.12503  | Val Loss: 0.2942 Accuracy: 0.9043\n",
      "Epoch: 82/160 Train Loss: 0.1429 Accuracy: 0.9507 Time: 31.14141  | Val Loss: 0.2972 Accuracy: 0.9060\n",
      "Epoch: 83/160 Train Loss: 0.1295 Accuracy: 0.9566 Time: 31.12524  | Val Loss: 0.2974 Accuracy: 0.9062\n",
      "Epoch: 84/160 Train Loss: 0.1248 Accuracy: 0.9572 Time: 31.13964  | Val Loss: 0.2997 Accuracy: 0.9080\n",
      "Epoch: 85/160 Train Loss: 0.1187 Accuracy: 0.9594 Time: 31.13252  | Val Loss: 0.3085 Accuracy: 0.9074\n",
      "Epoch: 86/160 Train Loss: 0.1134 Accuracy: 0.9612 Time: 31.13111  | Val Loss: 0.3129 Accuracy: 0.9088\n",
      "Epoch: 87/160 Train Loss: 0.1069 Accuracy: 0.9638 Time: 31.12980  | Val Loss: 0.3147 Accuracy: 0.9097\n",
      "Epoch: 88/160 Train Loss: 0.1063 Accuracy: 0.9625 Time: 31.14330  | Val Loss: 0.3162 Accuracy: 0.9104\n",
      "Epoch: 89/160 Train Loss: 0.1019 Accuracy: 0.9641 Time: 31.13018  | Val Loss: 0.3199 Accuracy: 0.9103\n",
      "Epoch: 90/160 Train Loss: 0.0973 Accuracy: 0.9660 Time: 31.13363  | Val Loss: 0.3284 Accuracy: 0.9073\n",
      "Epoch: 91/160 Train Loss: 0.0943 Accuracy: 0.9668 Time: 31.13716  | Val Loss: 0.3255 Accuracy: 0.9068\n",
      "Epoch: 92/160 Train Loss: 0.0921 Accuracy: 0.9674 Time: 31.12387  | Val Loss: 0.3324 Accuracy: 0.9083\n",
      "Epoch: 93/160 Train Loss: 0.0909 Accuracy: 0.9674 Time: 31.17513  | Val Loss: 0.3311 Accuracy: 0.9098\n",
      "Epoch: 94/160 Train Loss: 0.0900 Accuracy: 0.9685 Time: 31.13642  | Val Loss: 0.3366 Accuracy: 0.9081\n",
      "Epoch: 95/160 Train Loss: 0.0839 Accuracy: 0.9708 Time: 31.15169  | Val Loss: 0.3472 Accuracy: 0.9096\n",
      "Epoch: 96/160 Train Loss: 0.0840 Accuracy: 0.9702 Time: 31.12169  | Val Loss: 0.3360 Accuracy: 0.9110\n",
      "Epoch: 97/160 Train Loss: 0.0801 Accuracy: 0.9723 Time: 31.14026  | Val Loss: 0.3448 Accuracy: 0.9091\n",
      "Epoch: 98/160 Train Loss: 0.0788 Accuracy: 0.9725 Time: 31.15391  | Val Loss: 0.3443 Accuracy: 0.9089\n",
      "Epoch: 99/160 Train Loss: 0.0766 Accuracy: 0.9742 Time: 31.14190  | Val Loss: 0.3437 Accuracy: 0.9096\n",
      "Epoch: 100/160 Train Loss: 0.0794 Accuracy: 0.9721 Time: 31.15028  | Val Loss: 0.3482 Accuracy: 0.9115\n",
      "Epoch: 101/160 Train Loss: 0.0753 Accuracy: 0.9737 Time: 31.14320  | Val Loss: 0.3503 Accuracy: 0.9105\n",
      "Epoch: 102/160 Train Loss: 0.0711 Accuracy: 0.9748 Time: 31.15201  | Val Loss: 0.3660 Accuracy: 0.9062\n",
      "Epoch: 103/160 Train Loss: 0.0737 Accuracy: 0.9744 Time: 31.14607  | Val Loss: 0.3639 Accuracy: 0.9083\n",
      "Epoch: 104/160 Train Loss: 0.0685 Accuracy: 0.9760 Time: 31.15933  | Val Loss: 0.3664 Accuracy: 0.9067\n",
      "Epoch: 105/160 Train Loss: 0.0684 Accuracy: 0.9766 Time: 31.13075  | Val Loss: 0.3780 Accuracy: 0.9030\n",
      "Epoch: 106/160 Train Loss: 0.0678 Accuracy: 0.9757 Time: 31.16312  | Val Loss: 0.3625 Accuracy: 0.9089\n",
      "Epoch: 107/160 Train Loss: 0.0682 Accuracy: 0.9764 Time: 31.16913  | Val Loss: 0.3702 Accuracy: 0.9061\n",
      "Epoch: 108/160 Train Loss: 0.0655 Accuracy: 0.9778 Time: 31.12603  | Val Loss: 0.3810 Accuracy: 0.9064\n",
      "Epoch: 109/160 Train Loss: 0.0650 Accuracy: 0.9769 Time: 31.14615  | Val Loss: 0.3775 Accuracy: 0.9068\n",
      "Epoch: 110/160 Train Loss: 0.0638 Accuracy: 0.9778 Time: 31.13169  | Val Loss: 0.3831 Accuracy: 0.9084\n",
      "Epoch: 111/160 Train Loss: 0.0642 Accuracy: 0.9775 Time: 31.15548  | Val Loss: 0.3993 Accuracy: 0.9056\n",
      "Epoch: 112/160 Train Loss: 0.0622 Accuracy: 0.9780 Time: 31.12844  | Val Loss: 0.3834 Accuracy: 0.9083\n",
      "Epoch: 113/160 Train Loss: 0.0610 Accuracy: 0.9788 Time: 31.14618  | Val Loss: 0.3729 Accuracy: 0.9086\n",
      "Epoch: 114/160 Train Loss: 0.0612 Accuracy: 0.9788 Time: 31.13660  | Val Loss: 0.3795 Accuracy: 0.9065\n",
      "Epoch: 115/160 Train Loss: 0.0592 Accuracy: 0.9797 Time: 31.16226  | Val Loss: 0.3861 Accuracy: 0.9054\n",
      "Epoch: 116/160 Train Loss: 0.0580 Accuracy: 0.9800 Time: 31.17955  | Val Loss: 0.3831 Accuracy: 0.9087\n",
      "Epoch: 117/160 Train Loss: 0.0579 Accuracy: 0.9801 Time: 31.13557  | Val Loss: 0.3886 Accuracy: 0.9078\n",
      "Epoch: 118/160 Train Loss: 0.0558 Accuracy: 0.9801 Time: 31.16075  | Val Loss: 0.3945 Accuracy: 0.9076\n",
      "Epoch: 119/160 Train Loss: 0.0548 Accuracy: 0.9802 Time: 31.13923  | Val Loss: 0.3989 Accuracy: 0.9076\n",
      "Epoch: 120/160 Train Loss: 0.0575 Accuracy: 0.9797 Time: 31.14455  | Val Loss: 0.4036 Accuracy: 0.9044\n",
      "Epoch: 121/160 Train Loss: 0.0488 Accuracy: 0.9836 Time: 31.14181  | Val Loss: 0.3870 Accuracy: 0.9082\n",
      "Epoch: 122/160 Train Loss: 0.0454 Accuracy: 0.9846 Time: 31.13776  | Val Loss: 0.3845 Accuracy: 0.9084\n",
      "Epoch: 123/160 Train Loss: 0.0429 Accuracy: 0.9856 Time: 31.15925  | Val Loss: 0.3849 Accuracy: 0.9092\n",
      "Epoch: 124/160 Train Loss: 0.0449 Accuracy: 0.9849 Time: 31.14123  | Val Loss: 0.3866 Accuracy: 0.9096\n",
      "Epoch: 125/160 Train Loss: 0.0435 Accuracy: 0.9855 Time: 31.14701  | Val Loss: 0.3863 Accuracy: 0.9093\n",
      "Epoch: 126/160 Train Loss: 0.0416 Accuracy: 0.9858 Time: 31.13534  | Val Loss: 0.3864 Accuracy: 0.9096\n",
      "Epoch: 127/160 Train Loss: 0.0414 Accuracy: 0.9857 Time: 31.13364  | Val Loss: 0.3895 Accuracy: 0.9101\n",
      "Epoch: 128/160 Train Loss: 0.0404 Accuracy: 0.9867 Time: 31.12881  | Val Loss: 0.3898 Accuracy: 0.9105\n",
      "Epoch: 129/160 Train Loss: 0.0405 Accuracy: 0.9857 Time: 31.14233  | Val Loss: 0.3900 Accuracy: 0.9115\n",
      "Epoch: 130/160 Train Loss: 0.0396 Accuracy: 0.9869 Time: 31.14639  | Val Loss: 0.3917 Accuracy: 0.9108\n",
      "Epoch: 131/160 Train Loss: 0.0401 Accuracy: 0.9864 Time: 31.13472  | Val Loss: 0.3946 Accuracy: 0.9110\n",
      "Epoch: 132/160 Train Loss: 0.0387 Accuracy: 0.9871 Time: 31.12878  | Val Loss: 0.3958 Accuracy: 0.9107\n",
      "Epoch: 133/160 Train Loss: 0.0394 Accuracy: 0.9868 Time: 31.13106  | Val Loss: 0.3932 Accuracy: 0.9108\n",
      "Epoch: 134/160 Train Loss: 0.0405 Accuracy: 0.9860 Time: 31.12700  | Val Loss: 0.3988 Accuracy: 0.9107\n",
      "Epoch: 135/160 Train Loss: 0.0383 Accuracy: 0.9867 Time: 31.15979  | Val Loss: 0.3967 Accuracy: 0.9098\n",
      "Epoch: 136/160 Train Loss: 0.0386 Accuracy: 0.9871 Time: 31.16467  | Val Loss: 0.3949 Accuracy: 0.9106\n",
      "Epoch: 137/160 Train Loss: 0.0388 Accuracy: 0.9871 Time: 31.15024  | Val Loss: 0.3993 Accuracy: 0.9102\n",
      "Epoch: 138/160 Train Loss: 0.0374 Accuracy: 0.9873 Time: 31.14656  | Val Loss: 0.4006 Accuracy: 0.9112\n",
      "Epoch: 139/160 Train Loss: 0.0369 Accuracy: 0.9881 Time: 31.12024  | Val Loss: 0.3987 Accuracy: 0.9112\n",
      "Epoch: 140/160 Train Loss: 0.0387 Accuracy: 0.9869 Time: 31.15437  | Val Loss: 0.3996 Accuracy: 0.9112\n",
      "Epoch: 141/160 Train Loss: 0.0372 Accuracy: 0.9878 Time: 31.13367  | Val Loss: 0.4010 Accuracy: 0.9109\n",
      "Epoch: 142/160 Train Loss: 0.0390 Accuracy: 0.9870 Time: 31.16431  | Val Loss: 0.4022 Accuracy: 0.9116\n",
      "Epoch: 143/160 Train Loss: 0.0374 Accuracy: 0.9871 Time: 31.15074  | Val Loss: 0.4031 Accuracy: 0.9102\n",
      "Epoch: 144/160 Train Loss: 0.0367 Accuracy: 0.9876 Time: 31.14473  | Val Loss: 0.4025 Accuracy: 0.9110\n",
      "Epoch: 145/160 Train Loss: 0.0368 Accuracy: 0.9876 Time: 31.13833  | Val Loss: 0.4040 Accuracy: 0.9108\n",
      "Epoch: 146/160 Train Loss: 0.0377 Accuracy: 0.9873 Time: 31.14858  | Val Loss: 0.4010 Accuracy: 0.9126\n",
      "Epoch: 147/160 Train Loss: 0.0364 Accuracy: 0.9878 Time: 31.16086  | Val Loss: 0.3991 Accuracy: 0.9117\n",
      "Epoch: 148/160 Train Loss: 0.0353 Accuracy: 0.9882 Time: 31.14519  | Val Loss: 0.4060 Accuracy: 0.9120\n",
      "Epoch: 149/160 Train Loss: 0.0364 Accuracy: 0.9874 Time: 31.15944  | Val Loss: 0.4056 Accuracy: 0.9116\n",
      "Epoch: 150/160 Train Loss: 0.0353 Accuracy: 0.9881 Time: 31.13801  | Val Loss: 0.4104 Accuracy: 0.9109\n",
      "Epoch: 151/160 Train Loss: 0.0355 Accuracy: 0.9879 Time: 31.13516  | Val Loss: 0.4078 Accuracy: 0.9102\n",
      "Epoch: 152/160 Train Loss: 0.0377 Accuracy: 0.9879 Time: 31.14293  | Val Loss: 0.4101 Accuracy: 0.9119\n",
      "Epoch: 153/160 Train Loss: 0.0365 Accuracy: 0.9881 Time: 31.13184  | Val Loss: 0.4126 Accuracy: 0.9116\n",
      "Epoch: 154/160 Train Loss: 0.0356 Accuracy: 0.9880 Time: 31.13063  | Val Loss: 0.4090 Accuracy: 0.9107\n",
      "Epoch: 155/160 Train Loss: 0.0359 Accuracy: 0.9876 Time: 31.15536  | Val Loss: 0.4057 Accuracy: 0.9110\n",
      "Epoch: 156/160 Train Loss: 0.0331 Accuracy: 0.9888 Time: 31.13231  | Val Loss: 0.4111 Accuracy: 0.9090\n",
      "Epoch: 157/160 Train Loss: 0.0343 Accuracy: 0.9883 Time: 31.13323  | Val Loss: 0.4084 Accuracy: 0.9110\n",
      "Epoch: 158/160 Train Loss: 0.0346 Accuracy: 0.9882 Time: 31.14302  | Val Loss: 0.4153 Accuracy: 0.9095\n",
      "Epoch: 159/160 Train Loss: 0.0363 Accuracy: 0.9881 Time: 31.12471  | Val Loss: 0.4104 Accuracy: 0.9109\n",
      "Epoch: 160/160 Train Loss: 0.0358 Accuracy: 0.9881 Time: 31.15182  | Val Loss: 0.4061 Accuracy: 0.9114\n",
      "#Parameter: 42512970 Accuracy: 0.9114\n"
     ]
    }
   ],
   "source": [
    "from hdd.models.cnn.resnet import resnet101_config\n",
    "\n",
    "net, resnet101_stats = train_net(\n",
    "    resnet101_config,\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    dropout=0.5,\n",
    "    lr=0.01,\n",
    "    weight_decay=1e-1,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "432e2801",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "fields = resnet18_stats.keys()\n",
    "for i, field in enumerate(fields):\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    plt.plot(resnet18_stats[field], label=\"Resnet18\", linestyle=\"--\")\n",
    "    plt.plot(resnet34_stats[field], label=\"Resnet34\")\n",
    "    plt.plot(resnet50_stats[field], label=\"Resnet50\", linestyle=\"--\")\n",
    "    plt.plot(resnet101_stats[field], label=\"Resnet101\", linestyle=\"-\")\n",
    "    plt.legend()\n",
    "    plt.title(field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e567083",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-cu124",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
